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References

Published online by Cambridge University Press:  19 May 2022

Cheng Hsiao
Cheng Hsiao
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University of Southern California
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Analysis of Panel Data , pp. 469 - 497
Publisher: Cambridge University Press
Print publication year: 2022

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References

Abadie, A., Athey, S., Imbens, G.W., and Wooldridge, J.M. (2020). “Sampling Based versus Design Based Uncertainty in Regression Analysis,” Econometrica, 88, 265–296.CrossRefGoogle Scholar
Abadie, A., Diamond, A. and Hainmueller, J. (2010). “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program,” Journal of the American Statistical Association 105, 493–505.CrossRefGoogle Scholar
Abraham, K., Jarmin, R.S., Moyer, B.C., and Shapiro, M., ed. (2021). Big Data for Twenty-First Century Economic Statistics, Chicago: University of Chicago Press.Google Scholar
Abramowitz, M., and Stegun, J. (1965). Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables, New York: Dover.Google Scholar
Acemoglu, D., Akcigit, U. and Kerr, W. (2016). “Networks and the Macroeconomy: An Empirical Exploration,” in NBER Macroeconomics Annual 2015, edited by Eichenbaum, M. and Parker, J., Vol. 30, Chapter 4, pp. 276–335, Chicago: University of Chicago Press.Google Scholar
Ahn, H., and Horenstein, A.R. (2013). “Eigenvalue Ration Test for the Number of Factors,” Econometrica, 81(3), 1203–1227.Google Scholar
Ahn, H., and Powell, J.L. (1993). “Semiparametric Estimation of Censored Selection Models with a Nonparametric Selection Mechanism,” Journal of Econometrics, 58, 3–30.CrossRefGoogle Scholar
Ahn, S.C. (2015). “Comment on ‘IV Estimation of Panels with Factor Residuals,” Journal of Econometrics, 185, 525–544.CrossRefGoogle Scholar
Ahn, S.C., and Moon, H.R. (2001). “On Large-N and Large-T Properties of Panel Data Estimators and the Hausman Test,” mimeo.CrossRefGoogle Scholar
Ahn, S.C., and Schmidt, P. (1995). “Efficient Estimation of Models for Dynamic Panel Data,” Journal of Econometrics, 68, 5–27.CrossRefGoogle Scholar
Ahn, S.C., Lee, Y.H., and Schmidt, P. (2001). “GMM Estimation of Linear Panel Data Models with Time-Varying Individual Effects,” Journal of Econometrics, 101, 219–255.CrossRefGoogle Scholar
Ahn, S.C., Lee, Y.H., and Schmidt, P. (2013). “Panel Data Models with Multiple Time-Varying Individual Effects,” Journal of Econometrics, 174, 1–14.CrossRefGoogle Scholar
Ai, C., and Li, Q. (2005). “Estimation of Partly Specified Panel Data Tobit Models,” mimeo.Google Scholar
Ai, C., and Li, Q. (2008). “Semi-Parametric and Non-Parametric Models in Panel Data Models,” in The Econometrics of Panel Data, 3rd ed., edited by Matyas, L. and Sevestre, P., pp. 451–478, Berlin: Springer-Verlag.CrossRefGoogle Scholar
Aigner, D.J., and Balestra, P. (1988). “Optimal Experimental Design for Error Components Models,” Econometrica, 56, 955–972.CrossRefGoogle Scholar
Aigner, D.J., Hsiao, C., Kapteyn, A., and Wansbeek, T. (1984). “Latent Variable Models in Econometrics,” in Handbook of Econometrics, vol. II, edited by Griliches, Z. and Intriligator, M., pp. 1322–1393, Amsterdam: North-Holland.Google Scholar
Akaike, H. (1973). “Information Theory and an Extension of the Maximum Likelihood Principle,” in Proceedings of the 2nd. International Symposium Information Theory, edited by Petrov, B.N. and Csaki, F., pp. 267–281, Budapest: Akademiai Kiado.Google Scholar
Akashi, K., and Kunitomo, N. (2011). “The Limited Information Maximum Likelihood Approach to Dynamic Structural Equation Models,” mimeo.Google Scholar
Akashi, K., and Kunitomo, N. (2012). “Some Properties of the LIML Estimator in a Dynamic Panel Structural Equation,” Journal of Econometrics, 166, 167–183.CrossRefGoogle Scholar
Alessi, L., Barigozzi, M., and Capasso, M. (2010). “Improved Penalization for Determining the Number of Factors in Approximate Factor Models,” Statistics Probability Letters, 80, 1806–1813.CrossRefGoogle Scholar
Almon, S. (1965). “The Distributed Lag Between Capital Approximations and Expenditures,” Econometrica, 33, 178–196.Google Scholar
Alvarez, J., and Arellano, M. (2003). “The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators,” Econometrica, 71, 1121–1159.Google Scholar
Amemiya, T. (1971). “The Estimation of the Variance in a Variance-Component Model,” International Economic Review, 12, 1–13.CrossRefGoogle Scholar
Amemiya, T. (1974). “Bivariate Probit Analysis: Minimum Chi-Square Methods,” Journal of the American Statistical Association, 69, 940–944.CrossRefGoogle Scholar
Amemiya, T. (1976). “The Maximum Likelihood, the Minimum Chi-Square and the Nonlinear Weighted Least Squares Estimator in the General Qualitative Response Model,” Journal of the American Statistical Association, 71, 347–351.CrossRefGoogle Scholar
Amemiya, T. (1978). “A Note on a Random Coefficients Model,” International Economic Review, 19, 793–796.CrossRefGoogle Scholar
Amemiya, T. (1980a). “Selection of Regressors,” International Economic Review, 21, 331–354.CrossRefGoogle Scholar
Amemiya, T. (1980b). “The n−2-Order Mean Squared Errors of the Maximum Likelihood and the Minimum Logit Chi-Square Estimator,” Annals of Statistics 8, 488–505.CrossRefGoogle Scholar
Amemiya, T. (1981). “Qualitative Response Models: A Survey,” Journal of Economic Literature, 19, 1483–1536.Google Scholar
Amemiya, T. (1984). “Tobit Models: A Survey,” Journal of Econometrics, 24, 3–62.CrossRefGoogle Scholar
Amemiya, T. (1985). Advanced Theory of Econometrics, Cambridge: Harvard University Press.Google Scholar
Amemiya, T., and Fuller, W.A. (1967). “A Comparative Study of Alternative Estimators in a Distributed-Lag Model,” Econometrica, 35, 509–529.Google Scholar
Amemiya, T., and MaCurdy, T.E. (1986). “Instrumental Variable Estimation of An Error Components Model,” Econometrica, 54, 869–880.CrossRefGoogle Scholar
Amemiya, T., and Wu, R.Y. (1972). “The Effect of Aggregation on Prediction in the Autoregressive Model,” Journal of the American Statistical Association, 67(339), 628–632.CrossRefGoogle Scholar
Amemiya, T., and Wu, R.Y. (1970). “Asymptotic Properties of Conditional Maximum Likelihood Estimators,” Journal of the Royal Statistical Society, Series B, 32, 283–301.Google Scholar
Amemiya, T., and Wu, R.Y. (1973). Conditional Inference and Models for Measuring, Københarn: Mental-hygiejnish Farlag.Google Scholar
Amemiya, T., and Wu, R.Y. (1959). “On Asymptotic Distributions of Estimates of Parameters of Stochastic Differences Equations,” Annals of Mathematical Statistics 30, 676–687.Google Scholar
Amemiya, T., and Wu, R.Y. (1969). “Statistical Inference for Covariance Matrices with Linear Structure,” in Multivariate Analysis, vol. 2, edited by Krishnaiah, P.R., pp. 55–66, New York: Academic Press.Google Scholar
Amemiya, T., and Wu, R.Y. (1970). “Estimation of Covariance Matrices Which Are Linear Combinations or Whose Inverses Are Linear Combinations of Given Matrices,” in Essays in Probability and Statistics, edited by Bose, R.C., pp. 1–24, Chapel Hill: University of North Carolina Press.Google Scholar
Amemiya, T., and Wu, R.Y. (1971). The Statistical Analysis of Time Series. New York: Wiley.Google Scholar
Amemiya, T., and Wu, R.Y. (1978). “Repeated Measurements on Autoregressive Processes,” Journal of the American Statistical Association, 73, 371–378.Google Scholar
Amemiya, T., and Wu, R.Y. (1985). An Introduction to Multivariate Analysis, 2nd ed., New York: John Wiley and Sons.Google Scholar
Anderson, T.W., and Hsiao, C. (1981). “Estimation of Dynamic Models with Error Components,” Journal of the American Statistical Association, 76, 598–606.CrossRefGoogle Scholar
Anderson, T.W., and Hsiao, C. (1982). “Formulation and Estimation of Dynamic Models Using Panel Data,”Journal of Econometrics, 18, 47–82.CrossRefGoogle Scholar
Anderson, T.W., and Rubin, H. (1950). “The Asymptotic Properties of Estimates of the Parameters of a Single Equation in a Complete System of Stochastic Equations,” Annals of Mathematical Statistics, 21(4), 570–582.Google Scholar
Anderson, T.W., and Rubin, H. (1956). “Statistical Inference in Factor Analysis,” Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 5, 111–150.Google Scholar
Ando, T., and Bai, J. (2020). “Quantile Co-Movement in Financial Markets: A Panel Quantile Model with Unobserved Heterogeneity,” Journal of the American Statistical Association, 115, 266–279.Google Scholar
Anselin, L. (1988). Spatial Econometrics: Methods and Models, Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
Anselin, L., and Griffith, D.A. (1988). “Do Spatial Effects Really Matter in Regression Analysis?” Papers of the Regional Science Association, 65, 11–34.Google Scholar
Anselin, L., Le Gallo, J., and Jayet, H. (2008). “Spatial Panel Econometrics,” in The Econometrics of Panel Data, 3rd ed., edited by Matyas, L. and Sevestre, P., pp. 625–660, Berlin: Springer-Verlag.Google Scholar
Antweiler, W. (2001). “Nested Random Effects Estimation in Unbalanced Panel Data,” Journal of Econometrics, 101, 295–313.Google Scholar
Arellano, M. (2003). Panel Data Econometrics, Oxford: Oxford University Press.Google Scholar
Arellano, M., and Bond, S. (1991). “Some Tests of Specification for Panel Data: Monte Carlo Evidence and An Application to Employment Equations,” Review of Economic Studies, 58, 277–297.CrossRefGoogle Scholar
Arellano, M., and Bonhomme, S. (2009). “Robust Priors in Nonlinear Panel Data Models,” Econometrica, 77, 489–536.Google Scholar
Arellano, M., and Bonhomme, S. (2012). “Identifying Distributional Characteristics in Random Coefficients Panel Data Models,” Review of Economics Studies, 79, 987–1020.Google Scholar
Arellano, M., and Bover, O. (1995). “Another Look at the Instrumental Variable Estimation of Error-Components Models,” Journal of Econometrics, 68, 29–51.Google Scholar
Arellano, M., and Carrasco, R. (2003). “Binary Choice Panel Data Models with Predetermined Variables,” Journal of Econometrics, 357–381.Google Scholar
Arellano, M., and Honoré, B. (2001). “Panel Models: Some Recent Development,” in Handbook of Econometrics, vol. 5, edited by Heckman, J. and Leamer, E., pp. 3229–3296. Amsterdam: North-Holland.Google Scholar
Arellano, M., Bover, O., and Labeaga, J. (1999). “Autoregressive Models with Sample Selectivity for Panel Data,” in Analysis of Panels and Limited Dependent Variable Models, edited by Hsiao, C., Lahiri, K., Lee, L.F., and Pesaran, M.H., pp. 23–48, Cambridge: Cambridge University Press.Google Scholar
Ashenfelter, O., and Solon, G. (1982). “Longitudinal Labor Market Data-Sources, Uses and Limitations,” in What's Happening to American Labor Force and Productivity Measurements? pp. 109–126. Proceedings of a June 17, 1982, conference sponsored by the National Council on Employment Policy, W.E. Upjohn Institute for Employment Research.Google Scholar
Ashenfelter, O., Deaton, A., and Solon, G. (1984). “Does It Make Sense to Collect Panel Data in Developing Countries?” mimeo, World Bank.Google Scholar
Athey, S. (2018). “The Impact of Machine Learning on Economics,” mimeo, Stanford University.Google Scholar
Athey, S. (2019). “The Impact of Machine Learning on Economics.” In The Economics of Artificial Intelligence, University of Chicago Press.Google Scholar
Athey, S., and Imbens, G.W. (2019). “Machine Learning Methods We Economists Should Know About,” mimeo, Stanford University.CrossRefGoogle Scholar
Athey, S., Imbens, G., Pham, T., and Wager, S. (2017). “Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges,” ar Xiv: 1702.01250.Google Scholar
Avery, R.B. (1977). “Error Components and Seemingly Unrelated Regressions,” Econometrica, 45, 199–209.Google Scholar
Bai, J. (2009a). “Panel Data Models with Interactive Fixed Effects,” Econometrica, 77, 1229–1279.Google Scholar
Bai, J. (2009b). “Supplement to Panel Data Models with Interactive Fixed Effects, Technical Details and Proofs,” mimeo.Google Scholar
Bai, J., and Carrion-i-Silvestre, J. (2009). “Structural Changes, Common Stochastic Trends and Unit Roots in Panel Data,” Review of Economic Studies, 76, 471–501.CrossRefGoogle Scholar
Bai, J., and Ng, S. (2002). “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221.Google Scholar
Bai, J., and Ng, S. (2004). “A Panic on Unit Root Tests and Cointegration,” Econometrica, 72, 1127–1177.CrossRefGoogle Scholar
Bai, J., and Ng, S. (2010). “Panel Unit Root Tests with Cross-Section Dependence: A Further Investigation,” Econometric Theory, 26, 1088–1114.CrossRefGoogle Scholar
Bai, Z.D., and Saranadasa, H. (1996). “Effect of High Dimension: By an Example of a Two Sample Problem,” Statistical Sinica, 6, 311–329.Google Scholar
Bai, Z.D., and Silverstein, J.W. (2004). “CLT for Linear Spectral Statistics of Large-Dimensional Sample Covariance Matrices,” Annals of Probability, 32(1A), 553–605.CrossRefGoogle Scholar
Bailey, T., Jaggars, S.S., and Jenkins, D. (2015). Redesigning American Community Colleges: A Clearer Path to Student Success. Cambridge: Cambridge Harvard University Press.CrossRefGoogle Scholar
Baillie, R.T., and Baltagi, B.H. (1999). “Prediction from the Regression Model with One-Way Error Components,” in Analysis of Panels and Limited Dependent Variable Models, edited by Hsiao, C., Lahiri, K., Lee, L.F., and Pesaran, M.H., pp. 255–267, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Baker, S., Bloom, N., and Davis, S. (2016). “Measuring Economic Policy Uncertainty,” The Quarterly Journal of Economics, 131, 1593–1636.Google Scholar
Balazsi, L., Matyas, L., and Wansbeek, T. (2017). “Fixed Effects Models,” in The Econometrics of Multi-Dimensional Panels: Theory and Application, edited by Matyas, L., pp. 1–34, Springer, Switzerland.Google Scholar
Balestra, P., and Nerlove, M. (1966). “Pooling Cross-Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas,” Econometrica, 34, 585–612.Google Scholar
Baltagi, B.H. (1980). “On Seemingly Unrelated Regressions with Error Components,” Econometrica, 48, 1547–1551.Google Scholar
Baltagi, B.H. (1981a). “Simultaneous Equations with Error Components,” Journal of Econometrics 17, 189–200.CrossRefGoogle Scholar
Baltagi, B.H. (1981b). “Pooling: An Experimental Study of Alternative Testing and Estimation Procedures in a Two-Way Error Components Model,” mimeo, University of Houston.Google Scholar
Baltagi, B.H. (1995). Econometric Analysis of Panel Data, New York: Wiley.Google Scholar
Baltagi, B.H., and Griffin, J.M. (1983). “Gasoline Demand in the OECD: An Application of Pooling and Testing Procedures,” European Economic Review, 22, 117–137.Google Scholar
Baltagi, B.H., and Kao, C. (2000). “Nonstationary Panels, Cointegration in Panels and Dynamic Panels, A Survey in Nonstationary Panels, Panel Cointegration and Dynamic Panels,” Advances in Econometrics, vol. 15, edited by Baltagi, B., Amsterdam: JAI Press, 7–52.Google Scholar
Baltagi, B.H., and Li, Q. (1991). “A Transformation That Will Circumvent the Problem of Autocorrelation in an Error Component Model,” Journal of Econometrics, 48, 385–393.CrossRefGoogle Scholar
Baltagi, B.H., and Li, Q. (1992). “A Monotonic Property for Iterative GLS in the Two-Way Random Effects Model,” Journal of Econometrics, 53, 45–51.CrossRefGoogle Scholar
Baltagi, B.H., Song, S., and Jung, B. (2001). “The Unbalanced Nested Error Component Regression Model,” Journal of Econometrics, 101, 357–381.Google Scholar
Baltagi, B.H., Song, S., Jung, B., and Koh, W. (2007). “Testing for Serial Correlation, Spatial Autocorrelation and Random Effects Using Panel Data,” Journal of Econometrics, 140, 5–51.Google Scholar
Banerjee, A. (1999). “Panel Data Unit Roots and Cointegration: An Overview,” Oxford Bulletin of Economics and Statistics, 61, 607–629.Google Scholar
Banerjee, A., Marcellino, M., and Osbat, C. (2005). “Testing for PPP: Should We Use Panel Methods?” Empirical Economics, 30, 77–91.Google Scholar
Barro, R., and Sala-i-Martin, X. (1995). Economic Growth, New York: McGaw Hill.Google Scholar
Barth, J., Kraft, A., and Kraft, J. (1979). “A Temporal Cross-Section Approach to the Price Equation,” Journal of Econometrics 11, 335–351.CrossRefGoogle Scholar
Bartolucci, F., and Nigro, V. (2010). “A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting a Root-N Consistent Conditional Estimator,” Econometrica, 78, 719–733.Google Scholar
Bates, G., and Neyman, J. (1951). “Contributions to the Theory of Accident Proneness. II: True of False Contagion,” University of California Publications in Statistics, pp. 215–253.Google Scholar
Bates, J.M., and Granger, C.W.J. (1969). “The Combination of Forecasts,” Journal of the Operational Research Society, 20, 451–468.Google Scholar
Becketti, S., Gould, W., Lillard, L., and Welch, F. (1988). “The Panel Study of Income Dynamics after Fourteen Years: An Evaluation,” Journal of Labor Economics, 6, 472–492.Google Scholar
Beckwith, N. (1972). “Multivariate Analysis of Sales Response of Competing Brands to Advertising,” Journal of Marketing Research 9, 168–176.Google Scholar
Belloni, A., Chernozhukov, V., and Hansen, C. (2014a). “Structural and Treatment Effects,” The Journal of Economic Perspective, 28, 29–50.CrossRefGoogle Scholar
Belloni, A., Chernozhukov, V., and Hansen, C. (2014b). “Inference on Treatment Effects after Selection among High-Dimensional Controls,” The Review of Economic Studies, 81, 608–650.Google Scholar
Ben-Porath, Y. (1973). “Labor Force Participation Rates and the Supply of Labor,” Journal of Political Economy 81, 697–704.Google Scholar
Berkson, J. (1944). “Application of the Logistic Function to Bio-Assay,” Journal of the American Statistical Association, 39, 357–365.Google Scholar
Berkson, J. (1955). “Maximum Likelihood and Minimum χ2 Estimates of the Logistic Function,” Journal of the American Statistical Association, 50, 130–162.Google Scholar
Berkson, J. (1957). “Tables for Use in Estimating the Normal Distribution Function by Normit Analysis,” Biometrika, 44, 411–435.Google Scholar
Berkson, J. (1980). “Minimum Chi-Square, Not Maximum Likelihood!” Annals of Statistics, 8, 457–487.Google Scholar
Bernard, A., and Jones, C. (1996). “Productivity across Industries and Countries: Time Series Theory and Evidence,” Review of Economics and Statistics, 78, 135–146.Google Scholar
Bester, C.A., and Hansen, C.B. (2012). “Grouped Effects in Fixed Effects Models,” Journal of Econometrics, 190, 197–208.Google Scholar
Bhargava, A., and Sargan, J.D. (1983). “Estimating Dynamic Random Effects Models from Panel Data Covering Short Time Periods,” Econometrica, 51, 1635–1659.CrossRefGoogle Scholar
Binder, M., Hsiao, C., and Pesaran, M.H. (2005). “Estimation and Inference in Short Panel Vector Autoregression with Unit Roots and Cointegration,” Econometric Theory, 21, 795–837.CrossRefGoogle Scholar
Biørn, E (1992). “Econometrics of Panel Data with Measurement Errors,” in Econometrics of Panel Data: Theory and Applications, edited by Mátyás, L. and Sevestre, P., pp. 152–195, Kluwer.Google Scholar
Biørn, E (2000). “Panel Data with Measurement Errors, Instrumental Variables and GMM Estimators Combining Levels and Differences,” Econometric Reviews, 19, 391–424.Google Scholar
Biørn, E., and Klette, T.J. (1998). “Panel Data with Errors-in-Variables: Essential and Redundant Orthogonality Conditions in GMM Estimation,” Econometrics Letters, 59, 275–282.CrossRefGoogle Scholar
Biørn, E., and Krishnakumar, J. (2008). “Measurement Errors and Simultaneity,” in The Econometrics of Panel Data, 3rd ed., edited by Matyas, L. and Sevestre, P., pp. 323–368, Berlin: Spring-Verlag.Google Scholar
Bishop, Y.M., Fienberg, S.E., and Holland, P.W. (1975). Discrete Multivariate Analysis, Theory and Practice, Cambridge: MIT Press.Google Scholar
Blundell, R., and Bond, S. (1998). “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, 87, 115–143.Google Scholar
Blundell, R., and Smith, R.J. (1991). “Conditions Initiales et Estimation Efficace dans les Modéles Dynamiques sur Donné es de Panel,” Annals d’Economies et de Statistique, 20–21, 109–124.Google Scholar
Blundell, R., Browning, M., and Meghir, C. (1994). “Consumer Demand and the Life Cycle Allocation of Household Expenditure,” Review of Economic Studies, 61, 57–80.Google Scholar
Blundell, R., Griffith, R., and Windmeijer, F. (2002). “Individual Effects and Dynamics in Count Data Models,” Journal of Econometrics, 102, 113–131.CrossRefGoogle Scholar
Bond, S., and Meghir, C. (1994). “Dynamic Investment Models and the Firm's Financial Policy,” Review of Economic Studies, 61, 197–222.Google Scholar
Bonhomme, S., and Manresa, E. (2015). “Grouped Patterns of Heterogeneity in Panel Data,” Econometrica, 83, 1147–1184.Google Scholar
Bonhomme, S., Lamadon, T., and Manresa, E. (2019). “Discretizing Unobserved Heterogeneity,” mimeo.Google Scholar
Borus, M.E. (1981). “An Inventory of Longitudinal Data Sets of Interest to Economists,” mimeo, Ohio State University.Google Scholar
Bound, J. (1991). “Self-Reported Versus Objective Measures of Health in Retirement Models,” Journal of Human Resources, 26, 106–138.Google Scholar
Box, G.E.P., and Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day.Google Scholar
Box, G.E.P., and Tiao, G.C. (1968). “Bayesian Estimation of Means for the Random Effects Model,” Journal of the American Statistical Association, 63, 174–181.Google Scholar
Brainard, W.C., and Tobin, J. (1968). “Pitfalls in Financial Model Building,” American Economic Review, 58, 99–122.Google Scholar
Breiman, L. (1996). “Bagging Predictors,” Machine Learning, 24, 123–140.Google Scholar
Breiman, L. (2001). “Random Forests,” Machine Learning, 45, 5–32Google Scholar
Breitung, J., and Das, S. (2008). “Panel Unit Roots under Cross Sectional Dependence,” Statistica Nerrlandica, 59, 414–433.Google Scholar
Breitung, J., and Pesaran, M.H. (2008). “Unit Roots and Cointegration in Panels,” in The Econometrics of Panel Data, edited by Matyas, L. and Sevestre, P., pp. 279–322, Berlin: Spring-Verlag.Google Scholar
Bresson, G. (2020). “Comments on ‘An Econometrician's Perspective on Big Data’ by Cheng Hsiao,” Chapter 16, Advances in Econometrics, 41, 431–443.CrossRefGoogle Scholar
Bresson, G., and Hsiao, C. (2011). “A Functional Connectivity Approach for Modelling Cross-Sectional Dependence with an Application to the Estimation of Hedonic Housing Prices in Paris,” Advances in Statistical Analysis, 95, 501–509.Google Scholar
Bresson, G., Hsiao, C., and Pirotte, A. (2011). “Assessing the Contribution of R&D to Total Productivity – A Bayesian Approach to Account for Heterogeneity and Heteroscedasticity,” Advances in Statistical Analysis, 95, 435–452.CrossRefGoogle Scholar
Breusch, T.S. (1987). “Maximum Likelihood Estimation of Random Effects Models,” Journal of Econometrics, 36, 383–389.Google Scholar
Breusch, T.S., and Pagan, A.R. (1979). “A Simple Test for Heteroscedasticity and Random Coefficient Variation,” Econometrica, 47, 1287–1294.Google Scholar
Breusch, T.S., and Pagan, A.R. (1980). “The Lagrange Multiplier Test and His Application to Model Specification in Econometrics,” Review of Economic Studies, 47, 239–254.Google Scholar
Breusch, T.S., Mizon, G.E., and Schmidt, P. (1989). “Efficient Estimation Using Panel Data,” Econometrica, 51, 695–700.Google Scholar
Brock, W.A., and Durlauf, S.N. (2001). “Discrete Choice with Social Interactions,” The Review of Economics Studies, 68(2), 235–260.Google Scholar
Brock, W.A., and Durlauf, S.N. (2007). “Identification of Binary Choice Models with Social Interactions,” Journal of Econometrics, 140(1), 52–75.CrossRefGoogle Scholar
Burridge, P. (1980). “On the Cliff-Ord Test for Spatial Autocorrelation.” Journal of the Royal Statistical Society, Series B, 42, 107–108.Google Scholar
Butler, J.S., and Moffitt, R. (1982). “A Computationally Efficient Quadrature Procedure for the One Factor Multinominal Probit Model,” Econometrica, 50, 761–764.CrossRefGoogle Scholar
Cagan, P. (1958). “The Demand for Currency Relative to the Total Money Supply,” Journal of Political Economy, 66, 303–328.CrossRefGoogle Scholar
Cai, L., and Kalb, G. (2006). “Health Status and Labor Force Participation: Evidence from Australia,” Health Economics, 15, 241–256.Google Scholar
Cai, Z. (2007). “Trending Time-Varying Coefficient Time Series Models with Serially Correlated Errors,” Journal of Econometrics, 136, 163–188.Google Scholar
Cai, Z., Chen, L., and Fang, Y. (2018). “Quantile Panel Data Models with Partially Varying Coefficients,” Journal of Econometrics, 206, 531–553.Google Scholar
Campolieti, M. (2002). “Disability and the Labor Force Participation of Older Men in Canada,” Labor Economics, 9, 405–432.CrossRefGoogle Scholar
Canova, F. (1999). “Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach,” mimeo, Universitat Pompeu Fabra.Google Scholar
Card, D. (1996). “The Effect of Unions on the Structure of Wages: A Longitudinal Analysis,” Econometrica, 64, 957–979Google Scholar
Carro, J.M. (2007). “Estimating Dynamic Panel Data Discrete Choice Models with Fixed Effects,” Journal of Econometrics, 140, 503–528.Google Scholar
Case, A.C. (1991). “Spatial Patterns in Household Demand,” Econometrica, 59, 953–965.Google Scholar
Chamberlain, G. (1976). “Identification in Variance Components Models,” discussion paper No. 486, Harvard Institute of Economic Research.Google Scholar
Chamberlain, G. (1977a). “Education, Income, and Ability Revisited,” in Latent Variables in Socio-Economic Models, edited by Aigner, D.J. and Goldberger, A.S., pp. 143–161, Amsterdam: North-Holland.Google Scholar
Chamberlain, G. (1977b). “An Instrumental Variable Interpretation of Identification in Variance-Components and MIMIC Models,” in Kinometrics: Determinants of Social-Economic Success within and between Families, edited by Taubman, P., pp. 235–254, Amsterdam: North-Holland.Google Scholar
Chamberlain, G. (1978). “On the Use of Panel Data,” paper presented at the Social Science Research Council conference on life-cycle aspects of employment and the labor market, Mt. Kisco, New York.Google Scholar
Chamberlain, G. (1980). “Analysis of Covariance with Qualitative Data,” Review of Economic Studies, 47, 225–238.Google Scholar
Chamberlain, G. (1982). “Multivariate Regression Models for Panel Data,” Journal of Econometrics, 18, 5–46.Google Scholar
Chamberlain, G. (1984). “Panel Data,” in Handbook of Econometrics, vol. II, edited by Griliches, Z. and Intriligator, M., pp. 1247–1318, Amsterdam: North-Holland.Google Scholar
Chamberlain, G. (1992). “Efficiency Bounds for Semiparametric Regression,” Econometrica, 60, 567–596.Google Scholar
Chamberlain, G. (1993). “Feedback in Panel Data Models,” mimeo, Department of Economics, Harvard University.Google Scholar
Chamberlain, G. (2010). “Binary Response Models for Panel Data: Identification and Information,” Econometrica, 78, 159–168.Google Scholar
Chamberlain, G., and Griliches, Z. (1975). “Unobservables with a Variance-Components Structure: Ability, Schooling and the Economic Success of Brothers,” International Economic Review, 16, 422–450.Google Scholar
Chang, Y. (2002). “Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency,” Journal of Econometrics, 110, 261–292.Google Scholar
Chang, Y.S., Hu, B., and Park, J. (2018). “Econometric Analysis and Functional Dynamics in the Presence of Persistence,” paper presented at the conference in honor of P.C.B. Phillips, Yale University.Google Scholar
Charlier, E., Melenberg, B., and van Soest, A. (2000). “Estimation of a Censored Regression Panel Data Model Using Conditional Moment Restrictions Efficiently,” Journal of Econometrics, 95, 25–56.Google Scholar
Charlier, E., Melenberg, B., and van Soest, A. (2001). “An Analysis of Housing Expenditure Using Semiparametric Models and Panel Data,” Journal of Econometrics, 101, 71–108.Google Scholar
Chen, B., and Hong, Y. (2012). “Testing for Smooth Structural Changes in Time Series via Non Parametric Regression,” Econometrica, 80, 1157–1183.Google Scholar
Chen, J., Li, D., and Linton, O. (2019). “A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables,” Journal of Econometrics, 212, 155–176.Google Scholar
Chen, K.M., Hsieh, Y.W., and Lin, M.J. (2020). “Prediction and Inequality in Two Sided Markets: An Experiment of Online Dating Recommender Systems,” mimeo.Google Scholar
Chen, S. (1999). “Distribution-Free Estimation of the Random Coefficient Dummy Endogenous Variable Model,” Journal of Econometrics, 91, 171–199.Google Scholar
Chen, S. (2000). “Efficient Estimation of Binary Choice Models Under Symmetry,” Journal of Econometrics, 96, 183–199.CrossRefGoogle Scholar
Chen, S. (2019). “Quantile Regression for Duration Models with Time-Varying Regressors,” Journal of Econometrics, 209, 1–17.Google Scholar
Chen, X. (2007). “Large Sample Sieve Estimation of Semi-nonparametric Models,” in Handbook of Econometrics, vol. 6, pp. 5549–5632, Amsterdam: Elsevier.Google Scholar
Chen, X. (2018). “Large Sample Sieve Estimation of Semi-nonparametric Models,” in Handbook of Econometrics, vol. 6, 5549–5632. North Holland.Google Scholar
Chen, X., and Shen, X. (1998). “Sieve Extremum Estimates for Weakly Dependent Data,” Econometrica, 66, 289–314.Google Scholar
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., and Newey, W. (2017). “Double/Debiased/Neyman Machine Learning of Treatment Effects,” American Economic Review Papers and Proceedings, 107, 261–265.CrossRefGoogle Scholar
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., and Newey, W. (2018). “Double/Debiased Machine Learning for Treatment and Structural Parameters,” The Econometrics Journal, 21, 1–68.Google Scholar
Chernozhukov, V., Hausman, J., and Newey, W. (2018). “Demand Analysis with Many Prices,” paper presented at the 2nd Annual Econometrics Forum, University of the Chinese Academy of Sciences, Beijing.Google Scholar
Chesher, A.D. (1983). “The Information Matrix Test: Simplified Calculation via a Score Test Interpretation,” Economics Letters, 13, 45–48.Google Scholar
Chesher, A.D. (1984). “Testing for Neglected Heterogeneity,” Econometrica, 52, 865–872.CrossRefGoogle Scholar
Chesher, A.D., and Lancaster, T. (1983). “The Estimation of Models of Labor Market Behavior,” Review of Economic Studies, 50, 609–624.Google Scholar
Chiang, C.L. (1956). “On Regular Best Asymptotically Normal Estimates,” Annals of Mathematical Statistics, 27, 336–351.Google Scholar
Chintagunta, P., Kyriazidou, E., and Perktold, J. (2001). “Panel Data Analysis of Household Brand Choices,” Journal of Econometrics, 101, 111–153.Google Scholar
Choi, I. (2001). “Unit Root Tests for Panel Data,” Journal of International Money and Finance, 20, 249–272.Google Scholar
Choi, I. (2002a). “Combination Unit Root Tests for Cross-Sectionally Correlated Panels,” in Econometric Theory and Practice: Frontiers of Analysis and Applied Research, Essays in Honor of P.C.B. Phillips, Cambridge: Cambridge University Press.Google Scholar
Choi, I. (2002b). “Instrumental Variable Estimation of a Nearly Nonstationary, Heterogeneous Error Components Model,” Journal of Econometrics, 109, 1–32.Google Scholar
Choi, I. (2006). “Nonstationary Panels,” in Palgrave Handbooks of Econometrics, Vol. I, edited by Patterson, K. and Mills, T.C., pp. 511–539, New York: Palgrave Macmillan.Google Scholar
Choi, I., and Chue, T.K. (2007). “Subsampling Hypothesis Tests for Nonstationary Panels with Applications to Exchange Rates and Stock Prices,” Journal of Applied Econometrics, 22, 223–264.Google Scholar
Choo, E., and Siow, A. (2006). “Who Marries Whom and Why,” Journal of Political Economy, 114, 175–201.Google Scholar
Chow, G.C. (1983). Econometrics, New York: McGraw-Hill.Google Scholar
Chow, G., and Lin, A.L. (1976). “Best Linear Unbiased Estimation of Missing Observations in an Economic Time Series,” Journal of the American Statistical Association, 71, 719–721.CrossRefGoogle Scholar
Chudik, C., and Pesaran, M.H. (2015). “Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors,” Journal of Econometrics 188, 393–420.Google Scholar
Chudik, C., and Pesaran, M.H. (2020). “An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels,” mimeo.Google Scholar
Chudik, A., Kapetanios, A., and Pesaran, M.H. (2018). “A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High Dimensional Linear Regression Models,” Econometrica, 86, 1479–1512.Google Scholar
Chudik, A., Pesaran, M.H., and Tosetti, E. (2011). “Weak and Strong Cross Section Dependence and Estimation of Large Panels,” The Econometrics Journal, 14, 45–90.Google Scholar
Chui, C.K. (1992). An Introduction to Wavelets, San Diego: Academic Press.Google Scholar
Clarkson, D.B. (1979). “Estimating the Standard Errors of Rotated Factor Loadings by Jackknifing, Psychometrika, 44, 297–314.Google Scholar
Coleman, J.S. (1964). Models of Change and Response Uncertainty, Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Collado, M.D. (1997). “Estimating Dynamic Models from Time Series of Independent Cross-Sections,” Journal of Econometrics, 82, 37–62.Google Scholar
Conley, T.G. (1999). “GMM Estimation with Cross-sectional Dependence,” Journal of Econometrics, 92, 1–45.Google Scholar
Cooley, T.F., and Prescott, E.C. (1976). “Estimation in the Presence of Stochastic Parameter Variation,” Econometrica, 44, 167–184.Google Scholar
Cornwell, C., and Schmidt, P. (1984). “Panel Data with Cross-Sectional Variation in Slopes as Well as Intercepts,” mimeo. Michigan State University.Google Scholar
Cosslett, S.R. (1981). “Maximum Likelihood Estimator for Choice-Based Samples,” Econometrica, 49, 1289–1316.Google Scholar
Cox, D.R. (1970). Analysis of Binary Data, London: Methuen.Google Scholar
Cox, D.R. (1972). “The Analysis of Multivariate Binary Data,” Journal of the Royal Statistical Society, Series C, Applied Statistics, 21, 113–120.Google Scholar
Cox, D.R. (1975). “Partial Likelihood,” Biometrika, 62, 269–276.Google Scholar
Crépon, B., and Duget, E. (1997). “Estimating the Innovation from Patent Numbers: GMM on Count Panel Data,” Journal of Applied Econometrics, 12, 243–263.Google Scholar
Crépon, B., and Mairesse, J. (1996). “The Chamberlain Approach,” in The Econometrics of Panel Data: A Handbook of the Theory with Applications, edited by Matyas, L. and Sevestre, P., pp. 323–391, Dordrecht: Kluwer Academic Publishers.Google Scholar
Cripps, T., and Tarling, R. (1974). “An Analysis of the Duration of Male Unemployment in Great Britain, 1932–1973,” Economic Journal, 84, 289–316.Google Scholar
Damrongplasit, K., and Hsiao, C. (2009). “Decriminalization Policy and Marijuana Smoking Prevalence: A Look at the Literature,” Singapore Economic Review, 59, 621–644.CrossRefGoogle Scholar
Damrongplasit, K., and Hsiao, C. (2021). “Heterogeneity and Dynamic Dependence in Panel Analysis of Individual Behavior,” in Advances in Econometrics, vol 43. Emerald Publishing (forthcoming).Google Scholar
Damrongplasit, K., Hsiao, C., and Zhao, X. (2010). “Decriminalization and Marijuana Smoking Prevalence: Evidence from Australia,” Journal of Business and Economic Statistics, 28, 344–356.Google Scholar
Damrongplasit, K., Hsiao, C., and Zhao, X. (2019). “Health Status and Labor Market Outcome: Empirical Evidence from Australia”, Pacific Economic Review, 24, 269–292.CrossRefGoogle Scholar
Davis, P. (2002). “Estimating Multi-Way Error Components Models with Unbalanced Data Structures,” Journal of Econometrics, 106, 67–95.Google Scholar
Deaton, A. (1985). “Panel Data from Time Series of Cross-Sections,” Journal of Econometrics, 30, 109–126.Google Scholar
Debarsy, N., Jin, F., and Lee, L.F. (2015). “Large Sample Properties of the Matrix Exponential Specification with an Application to FDI,” Journal of Econometrics, 188, 1–21.CrossRefGoogle Scholar
De Finetti, B. (1964). “Foresight: Its Logical Laws, Its Subjective Sources”, in Studies in Subjective Probability, edited by Krieger, R.E., Huntington, New York.Google Scholar
Dehejia, R.H., and Wahba, S. (1999). “Propensity Score-Matching Methods for Nonexperimental Causal Studies,” The Review of Economics and Statistics, 84, 151–161.Google Scholar
Dempster, A.P. (1958). “A High Dimensional Two Sample Significance Test,” Annals of Mathematical Statistics, 29, 995–1010.Google Scholar
Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). “Maximum Likelihood From Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society, Series B, 39, 1–38.Google Scholar
Dhaene, G., and Jochmans, K. (2015). “Split-Panel Jackknife Estimation of Fixed-Effect Models. Review of Economic Studies, 82(3), 991–1030.Google Scholar
Dhrymes, P. (1971). Distributed Lags: Problems of Estimation and Formulation, San Francisco: Holden-Day.Google Scholar
Dickey, D.A., and Fuller, W.A. (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, 427–431.Google Scholar
Dickey, D.A., and Fuller, W.A. (1981). “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, 49, 1057–1072.CrossRefGoogle Scholar
Diebold, F.X., and Mariano, R. (1985). “Comparing Predictive Accuracy.” Journal of Business and Economic Statistics, 13, 253–265.Google Scholar
Dielman, T., Nantell, T., and Wright, R. (1980). “Price Effects of Stock Repurchasing: A Random Coefficient Regression Approach,” Journal of Financial and Quantitative Analysis, 15, 175–189.Google Scholar
Donald, S., and Newey, W. (2001). “Choosing the Number of Instruments,” Econometrica, 69, 1161–1191.Google Scholar
Driscoll, J., and Kraay, A. (1998). “Consistent Covariance Matrix Estimation with Spatically Dependent Panel Data,” Review of Economics and Statistics, 80, 549–560.Google Scholar
Duan, J.C., and Wang, T. (2012). “Measuring Distance-to-Default for Financial and Non-Financial Firms,” Global Credit Review, 2, 95–108.Google Scholar
Duan, J.C., Sun, J., and Wang, T. (2012). “Multiperiod Corporate Default Prediction – A Forward Intensity Approach,” Journal of Econometrics, 170, 191–209.Google Scholar
Dufour, J.M., and Hsiao, C. (2008). “Identification,” in the New Palgrave Dictionary of Economics, 2nd ed., edited by Blume, L. and Durlauf, S., Palgrave Macmillan.Google Scholar
Duncan, G.M. (1980). “Formulation and Statistical Analysis of the Mixed Continuous/Discrete Dependent Variable Model in Classical Production Theory,” Econometrica, 48, 839–152.Google Scholar
Durbin, J. (1960). “Estimation of Parameters in Time-Series Regression Models,” Journal of the Royal Statistical Society, Series B, 22, 139–153.Google Scholar
Durlauf, S.N. (2001). “Manifesto for a Growth Econometrics,” Journal of Econometrics, 100, 65–69.Google Scholar
Durlauf, S.N., and Johnson, P. (1995). “Multiple Regimes and Cross-Country Growth Behavior,” Journal of Applied Econometrics, 10, 365–384.Google Scholar
Durlauf, S.N., and Quah, D.T. (1999). “The New Empirics of Economic Growth,” in Handbook of Macroeconomics, edited by Taylor, J. and Woodford, M., pp. 235–308, Amsterdam: North-Holland.Google Scholar
Eicker, F. (1963). “Asymptotic Normality and Consistency of the Least Squares Estimators for Families of Linear Regression,” Annals of Mathematical Statistics, 34, 447–456.Google Scholar
Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans, SIAM, vol. 30.Google Scholar
Elliott, G., and Timmermann, A. (2017). Economic Forecasting, Princeton: Princeton University Press.Google Scholar
Engle, R.F., and Granger, C.W.J. (1987). “Cointegration and Error Correction: Representation, Estimation and Testing,” Econometrica, 55, 251–276.Google Scholar
Eurostat (1996). European Community Household Panel (ECHP), Office for Official Publications of the European Communities, Luxembourg.Google Scholar
Fan, J., and Gijbels, I. (1992). “Variable Bandwidth and Local Linear Regression Smoothers,” Annals of Statistics, 20, 2008–2036.Google Scholar
Fan, J., and Kim, D. (2018). “Robust High-Dimensional Volatility Matrix Estimation of High-Frequency Factor Model,” Journal of the American Statistical Association, 113, 1268–1283.Google Scholar
Fang, K.T. and Zhang, Y.T. (1990). Generalized Multivariate Analysis, Berlin: Springer.Google Scholar
Fan, Y., Lv, J., and Wang, J. (2018). “DNN: A Two-Scale Tale of Heterogenous Treatment Effects,” mimeo.Google Scholar
Fazzari, S.M., Hubbard, R.G., and Petersen, B.C. (1988). “Financing Constraints and Corporate Investment,” Brookings Papers on Economic Activity, 1, 141–195.Google Scholar
Ferguson, T.S. (1958). “A Method of Generating Best Asymptotically Normal Estimates with Application to the Estimation of Bacterial Densities,” Annals of Mathematical Statistics, 29, 1046–1162.Google Scholar
Firpo, S., Fortin, N., and Lemieux, T. (2007). “Decomposing Wage Distribution Using Recentered Influence Function Regressions,” unpublished manuscript.Google Scholar
Fisher, R.A. (1932). Statistical Methods for Research Workers, 4th ed., Edinburgh: Oliver and Boyd.Google Scholar
Florens, J.P., Fougére, D., and Mouchart, M. (1996). “Duration Models,” in The Econometrics of Panel Data, 2nd ed., edited by Matyas, L. and Sevestre, P., pp. 491–536, Dordrecht: Kluwer Academic Publishers.Google Scholar
Fomby, T.B. (2020). “Comments on ‘An Econometrician's Perspective on Big Data’ by Cheng Hsiao, Chapter 15, Advances in Econometrics, 41, 425–430.Google Scholar
Fougére, G., and Kamionka, T. (1996). “Individual Labour Market Transitions,” in The Econometrics of Panel Data: A Handbook of the Theory with Applications, 2nd ed., pp. 771–809, Dordrecht: Kluwer Academic Publishers.Google Scholar
Frankel, J.A., and Rose, A.K. (1996). “A Panel Project on Purchasing Power Parity: Mean Revision between and within Countries,” Journal of International Economics, 40, 209–244.Google Scholar
Freeman, R.B., and Medoff, J.L. (1981). “The Impact of Collective Bargaining: Illusion or Reality?” mimeo, Harvard University.Google Scholar
Friedman, M. (1953). Essays in Positive Economics, Chicago: University of Chicago Press.Google Scholar
Fujiki, H., and Hsiao, C. (2015). “Disentangling Multiple Treatment Effects – Measuring the Net Economic Impact of the 1995 Great Hanshin-Awaji Earthquake,” Journal of Econometrics 186, 66–73.Google Scholar
Fuller, W.A., and Battese, G.E. (1974). “Estimation of Linear Models with Cross-Error Structure,” Journal of Econometrics, 2, 67–78.Google Scholar
Gardeazabal, J., and Vega-Bayo, A. (2016). “An Empirical Comparison between the Synthetic Control Method and Hsiao et al.'s Panel Data Approach to Program Evaluation. Journal of Applied Econometrics, DOI: 10.1002/jae.2557.Google Scholar
Gelfand, A.E., and Smith, A.F.M. (1990). “Sampling-Based Approaches to Calculating Marginal Densities,” Journal of the American Statistical Association, 85, 398–409.Google Scholar
Geweke, J. (1991). “Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints,” Computer Science and Statistics: Proceedings of the Twenty Third Symposium on the Interface, 571–578.Google Scholar
Girma, S. (2000). “A Quasi-differencing Approach to Dynamic Modelling from a Time Series of Independent Cross-Sections,” Journal of Econometrics, 98, 365–383.Google Scholar
Goldberger, A.S. (1972). “Maximum Likelihood Estimation of Regressions Containing Unobservable Independent Variables,” International Economic Review, 13, 1–15.Google Scholar
Goodman, L.A. (1961). “Statistical Methods for the Mover-Stayer Model,” Journal of the American Statistical Association, 56, 841–868.Google Scholar
Goodrich, R.L., and Caines, P.E. (1979). “Linear System Identification from Nonstationary Cross-Sectional Data,” IEEE Transaction on Automatic Control, AC-24, 403–411.Google Scholar
Gorseline, D.E. (1932). The Effect of Schooling upon Income, Bloomington: Indiana University Press.Google Scholar
Gourieroux, C., and Monfort, A. (1996). Simulation-Based Econometric Methods, Oxford: Oxford University Press.Google Scholar
Gourieroux, C., Monfort, A., and Trognon, A. (1984). “Pseudo Maximum Likelihood Methods Applications to Poisson Models,” Econometrica, 52, 701–720.Google Scholar
Graham, B.S. (2016). Homophily and Transitivity in Dynamic Network Formation (No. w22186).Google Scholar
Graham, B.S., and Powell, J.L. (2012). “Identification and Estimation of Average Partial Effects in ‘Irregular’ Correlated Random Coefficient Panel Data Models,” Econometrica, 80, 2105–2152.Google Scholar
Graybill, F.A. (1969). Introduction to Matrices with Applications in Statistics, Belmont: Wadsworth.Google Scholar
Granger, C.W.J. (1980). “Long Memory Relationships and the Aggregation of Dynamic Models,” Journal of Econometrics, 14, 227–238.CrossRefGoogle Scholar
Granger, C.W.J., Maasoumi, E., and Racine, J.C. (2004). “A Dependence Metric for Possibly Nonlinear Processes,” Journal of Time Series Analysis, 25, 649–669.Google Scholar
Grassetti, L. (2011). “A Note on Transformed Likelihood Approach in Linear Dynamic Panel Models,” Statistical Methods and Applications, 20, 221–240.Google Scholar
Greenaway-McGrevy, R., Han, C., and Sul, D. (2012). “Asymptotic Distribution of Factor Augmented Estimators for Panel Regression,” Journal of Econometrics, 169, 48–53.Google Scholar
Griliches, Z. (1977). “Estimating the Returns to Schooling: Some Econometric Problems,” Econometrica, 45, 1–22.Google Scholar
Griliches, Z. (1979). “Sibling Models and Data in Economics: Beginning of a Survey,” Journal of Political Economy, 87 (Suppl. 2), S37–S64.Google Scholar
Griliches, Z., and Hausman, J. A. (1986). “Errors-in-Variables in Panel Data,” Journal of Econometrics, 31, 93–118.Google Scholar
Griliches, Z., Hall, B., and Hausman, J.A. (1978). “Missing Data and Self-selection in Large Panels,” Annales de l’INSEE, 30–31, 137–176.Google Scholar
Groen, J.J.J., and Kleibergen, F. (2003). “Likelihood-Based Cointegration Analysis in Panels of Vector Error-Correction Models,” Journal of Business and Economic Statistics, 21, 295–318.Google Scholar
Gronau, R. (1976). “The Allocation of Time of Israeli Women,” Journal of Political Economy, 84, 4, Part II.Google Scholar
Grunfeld, Y. (1958). “The Determinants of Corporate Investment,” unpublished Ph.D. thesis, University of Chicago.Google Scholar
Grunfeld, Y., and Griliches, Z. (1960). “Is Aggregation Necessarily Bad?” The Review of Economics and Statistics, 42, 1–13.Google Scholar
Gupta, A.K., and Varga, T. (1993). Elliptically Contour Models in Statistics, Kluwer.Google Scholar
Hadri, K. (2000). “Testing for Stationarity in Heterogeneous Panel Data,” Econometrics Journal, 3, 148–161.Google Scholar
Hadri, K., and Larsson, R. (2005). “Testing for Stationarity in Heterogeneous Panel Data Where the Time Dimension Is Fixed,” Econometric Journal, 8, 55–69.Google Scholar
Hahn, J. (1999). “How Informative Is the Initial Condition in a Dynamic Panel Model with Fixed Effects?” Journal of Econometrics, 93, 309–326.Google Scholar
Hahn, J., and Kuersteiner, G. (2002). “Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects When Both n and T Are Large,” Econometrica, 70, 1639–1657.Google Scholar
Hahn, J., and Kuersteiner, G. (2011). “Bias Reduction in Dynamic Nonnlinear Panel Data Models,” Econometric Theory, 27, 1152–1191.Google Scholar
Hahn, J., and Moon, H.R. (2006). “Reducing Bias of MLE in a Dynamic Panel Model,” Econometric Theory, 22, 499–512.Google Scholar
Hahn, J., and Newey, W. (2004). “Jackknife and Analytical Bias Reduction for Nonlinear Panel Models,” Econometrica, 72, 1295–1319.Google Scholar
Hahn, J., Hausman, J., and Kuersteiner, G. (2007). “Long Difference Instrumental Variable Estimation for Dynamic Models with Fixed Effects,” Journal of Econometrics, 140, 574–617.Google Scholar
Hall, P., Fisher, N.I., and Hoffman, B. (1992). “On the Nonparametric Estimation of Covariance Functions,” working paper, Australian National University.Google Scholar
Han, A., and Hausman, J.A. (1990). “Flexible Parametric Estimation of Duration and Competing Risk Models,” Journal of Applied Econometrics, 5, 1–28.Google Scholar
Han, C., and Phillips, P.C.B. (2013). “First Difference Maximum Likelihood and Dynamic Panel Estimation,” Journal of Econoemtrics, 175, 35–45.Google Scholar
Han, C., Phillips, P.C.B., and Sul, D. (2017). “Lag Length Selection in Panel Autoregression,” Econometric Reviews, 36(1–3), 225–240, DOI:10.1080/07474938.2015.1114313.Google Scholar
Han, X., Hsieh, C., and Ko, S. (2019). “Spatial Modeling Approach for Dynamic Network Formation and Interaction,” Journal of Business and Economic Statistics, DOI:1080/07350015.2019.1639395.Google Scholar
Hansen, B. (1982). “Efficient Estimation and Testing of Cointegrating Vectors in the Presence of Deterministic Trends,” Journal of Econometrics, 53, 87–121.Google Scholar
Hajivassiliou, V. (1990). “Smooth Simulation Estimation of Panel Data LDV Models,” mimeo, Yale University.Google Scholar
Härdle, W. (1990). Applied Nonparametric Regression, Econometric Society Monograph Series, 19, Cambridge: Cambridge University Press.Google Scholar
Harris, R.D.F., and Tzavalis, E. (1999). “Inference for Unit Roots in Dynamic Panels Where the Time Dimension Is Fixed,” Journal of Econometrics, 91, 201–226.Google Scholar
Hartley, H.O., and Rao, J.N.K. (1967). “Maximum Likelihood Estimation for the Mixed Analysis of Variance Model,” Biometrika, 54, 93–108.Google Scholar
Harvey, A.C. (1978). “The Estimation of Time-Varying Parameters from Panel Data,” Annales de l’INSEE, 30–31, 203–206.Google Scholar
Harvey, A.C., and Phillips, G.D.A. (1982). “The Estimation of Regression Models with Time-Varying Parameters,” in Games, Economic Dynamics, and Time Series Analysis, edited by Deistler, M., Fürst, E., and Schwödiauer, G.S., pp. 306–321, Cambridge: Physica-Verlag.Google Scholar
Hausman, J.A. (1978). “Specification Tests in Econometrics,” Econometrica, 46, 1251–1271.Google Scholar
Hausman, J.A., and McFadden, D. (1984). “Specification Tests for the Multinominal Logit Models,” Econometrica, 52, 1219–1240.Google Scholar
Hausman, J.A., and Taylor, W.E. (1981). “Panel Data and Unobservable Individual Effects,” Econometrica, 49, 1377–1398.Google Scholar
Hausman, J.A., and Wise, D. (1977). “Social Experimentation, Truncated Distributions, and Efficient Estimation,” Econometrica, 45, 919–938.Google Scholar
Hausman, J.A., and Wise, D. (1978). “A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences,” Econometrica, 46, 403–426.Google Scholar
Hausman, J.A., and Wise, D. (1979). “Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment,” Econometrica, 47, 455–473.Google Scholar
Hayashi, F. (1982). “Tobin's Marginal q and Average q: A Neoclassical Interpretation,” Econometrica, 50, 213–224.Google Scholar
Hayashi, K., and Sen, P.K. (1998). “On Covariance Estimators of Factor Loadings in Factor Analysis,” Journal of Multivariate Analysis, 66, 38–45.Google Scholar
Heckman, J.J. (1976). “The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models,” Annals of Economic and Social Measurement, 5, 475–492.Google Scholar
Heckman, J.J. (1978). “Simple Statistical Models for Discrete Panel Data Developed and Applied to Test the Hypothesis of True State Dependence Against the Hypothesis of Spurious State Dependence,” Annales e l’INSEE, 30–31, 227–269.Google Scholar
Heckman, J.J. (1979). “Sample Selection Bias as a Specification Error,” Econometrica, 47, 153–61.Google Scholar
Heckman, J.J. (1981a). “Statistical Models for Discrete Panel Data,” in Structural Analysis of Discrete Data with Econometric Applications, edited by Manski, C.F. and McFadden, D., pp. 114–178, Cambridge: MIT Press..Google Scholar
Heckman, J.J. (1981b). “The Incidental Parameters Problem and the Problem of Initial Conditions in Estimating a Discrete Time-Discrete Data Stochastic Process,” in Structural Analysis of Discrete Data with Econometric Applications, edited by Manski, C.F. and McFadden, D., pp. 179–195, Cambridge: MIT Press..Google Scholar
Heckman, J.J. (1981c). “Heterogeneity and State Dependence,” in Studies in Labor Markets, edited by Rosen, S., pp. 91–139, Chicago: University of Chicago Press.Google Scholar
Heckman, J.J. (1997). “Constructing Counterfactuals Under Different Assumptions,” mimeo, University of Chicago.Google Scholar
Heckman, J.J., and Robb, R. (1985). “Alternative Methods for Evaluating the Impact of Interventions,” in Longitudinal Analysis of Labor Market Data, edited by Heckman, J. and Singer, B., New York: Cambridge University Press.Google Scholar
Heckman, J.J., and Singer, B. (1984). “Econometric Duration Analysis,” Journal of Econometrics, 24, 63–132.Google Scholar
Heckman, J.J., and Vytlacil, E. (1998). “Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return Is Correlated with Schooling,” Journal of Human Resources, 33, 974–987.Google Scholar
Heckman, J.J., and Vytlacil, E. (2001). “Local Instrumental Variables,” in Nonlinear Statistical Inference, edited by Hsiao, C., Morimune, K., and Powell, J.L, New York: Cambridge University Press, 1–46.Google Scholar
Heckman, J.J., and Vytlacil, E. (2005). “Structural Equations, Treatment Effects and Economic Policy Evaluation,” Econometrica, 69, 669–738.Google Scholar
Heckman, J.J., and Vytlacil, E. (2007). “Econometric Evaluation of Social Programs,” in Handbook of Econometrics, vol. 6B, Amsterdam: North-Holland.Google Scholar
Heckman, J.J., and Willis, R. (1977). “A Beta-Logistic Model for the Analysis of Sequential Labor Force Participation by Married Women,” Journal of Political Economy, 85, 27–58.Google Scholar
Heckman, J.J., Schmierer, D.A., and Urzua, S.S. (2010). “Testing the Correalted Random Coefficient Model,” Journal of Econometrics, 158, 177–203.Google Scholar
Heckman, J.J., Urzua, S., and Vytlacil, E. (2006). “Understanding Instrumental Variables in Models with Essential Heterogeneity,” The Review of Economics and Statistics, 88, 389–432.Google Scholar
Henderson, C.R. Jr. (1971). “Comment on ‘The Use of Error Components Models in Combining Cross-Section with Time Series Data,” Econometrica, 39, 397–401.Google Scholar
Henderson, D.J., and Ullah, A. (2008). “Nonparametric Estimation in a One-Way Error Component Model: A Monte Carlo Analysis,” mimeo.Google Scholar
Hendricks, W., Koenker, R., and Poirier, D.J. (1979). “Residential Demand for Electricity: An Econometric Approach,” Journal of Econometrics, 9, 33–57.Google Scholar
Hildreth, C., and Houck, J.P. (1968). “Some Estimators for a Linear Model with Random Coefficients,” Journal of the American Statistical Association, 63, 584–595.Google Scholar
Hirano, K., Imbens, G.W., Ridder, G., and Rubin, D.B. (2001). “Combining Panel Data Sets with Attrition and Refreshment Samples,” Econometrica, 69, 1645–1660.Google Scholar
Hoch, I. (1962). “Estimation of Production Function Parameters Combining Time-Series and Cross-Section Data,” Econometrica, 30, 34–53.Google Scholar
Holly, A. (1982). “A Remark on Hausman's Specification Test,” Econometrica, 50, 749–759.Google Scholar
Holly, A., and Gardiol, L. (2000). “A Score Test for Individual Heteroscedasticity in a One-Way Error Components Model,” in Panel Data Econometrics, edited by Krishnakumkar, J. and Ronchetti, E., pp. 199–211, Amsterdam: North-Holland.Google Scholar
Holtz-Eakin, D., Newey, W., and Rosen, H.S. (1988). “Estimating Vector Autoregressions with Panel Data,” Econometrica, 56, 1371–1395.Google Scholar
Hood, W.C., and Koopmans, T.C. (eds) (1953). Studies in Econometric Method, Cowles Foundation Monographs 14, New York: Wiley.Google Scholar
Hong, Y., and Kao, C. (2004). “Wavelet-Based Testing for Serial Correlation of Unknown Form in Panel Models,” Econometrica, 72, 1519–1563.Google Scholar
Hong, Y., and White, H. (1995). “Consistent Specification Testing via Nonparametric Series Regression,” Econometrica, 63, 1133–1159.Google Scholar
Honoré, B.E. (1992). “Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects,” Econometrica, 60, 533–567.Google Scholar
Honoré, B.E. (1993). “Orthogonality Conditions for Tobit Models with Fixed Effects and Lagged Dependent Variables,” Journal of Econometrics, 59, 35–61.Google Scholar
Honoré, B.E., and Kesina, M. (2017). “Estimation of Some Nonlinear Panel Data Models with Both Time-Varying and Time-Invariant Explanatory Variables,” Journal of Business and Economic Statistics, 35, 543–558.Google Scholar
Honoré, B.E., and Kyriazidou, E. (2000). “Panel Data Discrete Choice Models with Lagged Dependent Variables,” Econometrica, 68, 839–874.Google Scholar
Honoré, B.E., and Tamer, E. (2006). “Bounds on Parameters in Panel Dynamic Discrete Choice Models,” Econometrica, 74, 611–629.Google Scholar
Horowitz, J.L. (1992). “A Smoothed Maximum Score Estimator for the Binary Response Model,” Econometrica, 60, 505–531.Google Scholar
Hotelling, H. (1931). “The Generalization of Student's Ratio,” Annals of Mathematical Statistics, 2, 360–378.Google Scholar
Hsiao, C. (1974a). “Statistical Inference for a Model with Both Random Cross-Sectional and Time Effects,” International Economic Review, 15, 12–30.Google Scholar
Hsiao, C. (1974b). “The Estimation of Labor Supply of Low Income Workers – Some Econometric Considerations,” Working Paper 970–1, The Urban Institute, Washington, DC.Google Scholar
Hsiao, C. (1975). “Some Estimation Methods for a Random Coefficients Model,” Econometrica, 43, 305–325.Google Scholar
Hsiao, C. (1976). “Regression Analysis with Limited Dependent Variable,” 1P-186, IBER and CRMS, University of California, Berkeley.Google Scholar
Hsiao, C. (1979a). “Causality Tests in Econometrics,” Journal of Economic Dynamics and Control, 1, 321–346.Google Scholar
Hsiao, C. (1979b). “Autoregressive Modelling of Canadian Money and Income Data,” Journal of the American Statistical Association, 74, 553–560.Google Scholar
Hsiao, C. (1979c). “Linear Regression Using Both Temporally Aggregated and Temporally Disaggregated Data,” Journal of Econometrics, 10, 243–252.Google Scholar
Hsiao, C. (1982). “Autoregressive Modelling and Causal Ordering of Economic Variables,” Journal of Economic Dynamics and Control, 4, 243–259.Google Scholar
Hsiao, C. (1983). “Identification,” in Handbook of Econometrics, vol. I, edited by Griliches, Z. and Intriligator, M., pp. 223–283, Amsterdam: North-Holland.Google Scholar
Hsiao, C. (1985a). “Benefits and Limitations of Panel Data,” Econometric Reviews, 4, 121–174.Google Scholar
Hsiao, C. (1985b). “Minimum Chi-Square,” in the Encyclopedia of Statistical Science, vol. 5, edited by Kotz, S. and Johnson, N., pp. 518–522, New York: Wiley.Google Scholar
Hsiao, C. (1989). “Consistent Estimation or Some Nonlinear Errors-in-Variables Models,” Journal of Econometrics, 41, 159–185.Google Scholar
Hsiao, C. (1991a). “A Mixed Fixed and Random Coefficients Framework for Pooling Cross-Section and Time Series Data,” paper presented at the Third Conference on Telecommunication Demand Analysis with Dynamic Regulation, Hilton Head, S Carolina, in New Development in Quantitative Economics, edited by Lee, J.W. and Zhang, S.Y., Beijing: Chinese Academic of Social Science.Google Scholar
Hsiao, C. (1991b). “Identification and Estimation of Latent Binary Choice Models Using Panel Data,” Review of Economic Studies, 58, 717–731.Google Scholar
Hsiao, C. (1992a). “Random Coefficients Models,” in The Econometrics of Panel Data, edited by Matyas, L. and Sevestre, P., Kluwer; 1st ed., pp. 223–241, 2nd ed. (1996), pp. 410–428.Google Scholar
Hsiao, C. (1992b). “Logit and Probit Models,” in The Econometrics of Panel Data, edited by Matyas, L. and Sevestre, P., Dordrecht: Kluwer Academic Publishers, 223–241.Google Scholar
Hsiao, C. (1995). “Panel Analysis for Metric Data,” in Handbook of Statistical Modelling in the Social and Behavioral Sciences, edited by Arminger, G., Clogg, C.C., and Sobel, M.Z., pp. 361–400, New York: Plenum.Google Scholar
Hsiao, C. (2001). “Economic Panel Data,” in International Encyclopedia of the Social and Behavioral Sciences, edited by Snelser, N.J. and Bates, P.B., vol. 6, 4114–4121, Oxford: Elsevier.Google Scholar
Hsiao, C. (2007). “Panel Data Analysis-Advantages and Challenges,” TEST, 16, 1–22.Google Scholar
Hsiao, C. (2014). Analysis of Panel Data, 3rd edition, Cambridge University Press.Google Scholar
Hsiao, C. (2018). “Panel Models with Interactive Effects,” Journal of Econometrics 206, 645–673.Google Scholar
Hsiao, C. (2020a). “An Econometrician's Perspective on Big Data,” Advances in Econometrics, 41, 413–423.Google Scholar
Hsiao, C. (2020b). “Estimation of Fixed Effects Dynamic Panel Data Models: Linear Differencing or Conditional Expectation,” Econometric Reviews, 39, 858–874.Google Scholar
Hsiao, C., and Pesaran, M.H. (2008). “Random Coefficients Models,” in The Econometrics of Panel Data, 3rd ed., edited by Matayas, L. and Sevestre, P., pp. 187–216, Berlin: Springer.Google Scholar
Hsiao, C., and Sun, B.H. (2000). “To Pool or Not to Pool Panel Data,” in Panel Data Econometrics: Future Directions, Papers in Honor of Professor Pietro Balestra, edited by Krishnakumar, J. and Ronchetti, E., pp. 181–198, Amsterdam: North-Holland.Google Scholar
Hsiao, C., and Tahmiscioglu, A.K. (1997). “A Panel Analysis of Liquidity Constraints and Firm Investment,” Journal of the American Statistical Association, 92, 455–465.Google Scholar
Hsiao, C., and Tahmiscioglu, A.K. (2008). “Estimation of Dynamic Panel Data Models with Both Individual and Time Specific Effects,” Journal of Statistical Planning and Inference, 138, 2698–2721.Google Scholar
Hsiao, C., and Taylor, G. (1991). “Some Remarks on Measurement Errors and the Identification of Panel Data Models,” Statistica Neerlandica, 45, 187–194.Google Scholar
Hsiao, C., and Wan, S.K. (2014). “Is There an Optimal Forecast Combination?” Journal of Econometrics 178, 294–309.Google Scholar
Hsiao, C., and Wang, K.Q. (2000). “Estimation of Structural Nonlinear Errors-in-Variables Models by Simulated Least Squares Method,” International Economic Review, 41, 523–542.Google Scholar
Hsiao, C., and Zhang, J. (2015). “IV, GMM or Likelihood Approach to Estimate Dynamic Panel Models When Either N or T or Both Are Large,” Journal of Econometrics, 187, 312–322.Google Scholar
Hsiao, C., and Zhou, Q. (2015). “Statistical Inference for Panel Dynamic Simultaneous Equations Models,” Journal of Econometrics, 189, 383–396.Google Scholar
Hsiao, C., and Zhou, Q. (2018). “Incidental Parameters, Initial Conditions and Sample Size in Statistical Inference for Dynamic Panel Data Models,” Journal of Econometrics, 207, 114–128.Google Scholar
Hsiao, C., and Zhou, Q. (2019). “Panel Parametric, Semi-parametric and Nonparametric Construction of Counterfactuals,” Journal of Applied Econometrics, 34, 463–481.Google Scholar
Hsiao, C., and Zhou, Q. (2020). “Estimation of Dynamic Panel Data Models with Interactive Effects: Pairwise or Quasi-differencing over Time,” mimeo.Google Scholar
Hsiao, C., and Zhou, Q. (2021). “Statistical Inference for the Low Dimensional Parameters of Linear Regression Models in the Presence of High Dimensional Data: An Orthogonal Projection Approach,” mimeo.Google Scholar
Hsiao, C., Appelbe, T.W., and Dineen, C.R. (1993). “A General Framework for Panel Data Analysis – with an Application to Canadian Customer Dialed Long Distance Service,” Journal of Econometrics, 59, 63–86.Google Scholar
Hsiao, C., Ching, H.S., and Wan, S. (2012). “A Panel Data Approach for Program Evaluation – Measuring the Benefits of Political and Economic Integration of Hong Kong with Mainland China,” Journal of Applied Econometrics, 27, 705–740.Google Scholar
Hsiao, C., Li, Q., Liang, Z., and Xie, W. (2019). “Panel Data Estimation for Correlated Random Coefficients Models,” Econometrics, 7(7).Google Scholar
Hsiao, C., Pesaran, M.H., and Pick, A. (2012). “Diagnostic Tests of Cross-Section Independence for Limited Dependent Variable Panel Data Models,” Oxford Bulletin of Economics and Statistics, 74, 253–277.Google Scholar
Hsiao, C., Pesaran, M.H., and Tahmiscioglu, A.K. (1999). “Bayes Estimation of Short-Run Coefficients in Dynamic Panel Data Models,” in Analysis of Panels and Limited Dependent Variables Models, edited by Hsiao, C., Lee, L.F., Lahiri, K., and Pesaran, M.H., pp. 268–296, Cambridge: Cambridge University Press.Google Scholar
Hsiao, C., Pesaran, M.H., and Tahmiscioglu, A.K. (2002). “Maximum Likelihood Estimation of Fixed Effects Dynamic Panel Data Models Covering Short Time Periods,” Journal of Econometrics, 109, 107–150.Google Scholar
Hsiao, C., Shen, Y., and Fujiki, H. (2005). “Aggregate vs. Disaggregate Data Analysis – A Paradox in the Estimation of Money Demand Function of Japan,” Journal of Applied Econometrics, 20, 579–601.Google Scholar
Hsiao, C., Shen, Y., and Zhou, Q. (2021). “Panel Data Approach for Measuring the Average Treatment Effects with Multiple Treated Units: To Aggregate or Not,” Advance in Econometrics, forthcoming.Google Scholar
Hsiao, C., Shi, Z., and Zhou, Q. (2021). “Transformed Estimation for Panel Interactive Effects Models,” Journal of Business and Economic Statistics (forthcoming).Google Scholar
Hsiao, C., Wang, L.Q., and Wang, K.Q. (1997). “Estimation of Nonlinear Errors-in-Variables Models – An Approximate Solution,” Statistical Papers, 38, 1–28.Google Scholar
Hsiao, C., Xie, Y., and Zhou, Q. (2021). “Factor Dimension Determination for Panel Interactive Effects Model: An Orthogonal Projection Approach,” Computational Statistics, 36, 1481–1497.Google Scholar
Hsiao, C., Li, Q., Liang, Z., and Xie, W. (2019). “Panel Data Estimation for Correlated Random Coefficients Models,” Econometrics, 7.Google Scholar
Hsiao, C., Mountain, D., Tsui, K.Y., and Chan, M.W.L. (1989). “Modeling Ontario Regional Electricity System Demand Using a Mixed Fixed and Random Coefficients Approach,” Regional Science and Urban Economics, 19, 567–587.Google Scholar
Hsiao, C., Nugent, J., Perrigne, I., and Qiu, J. (1998). “Shares versus Residual Claimant Contracts: The Case of Chinese TVEs,” Journal of Comparative Economics, 26, 317–337.Google Scholar
Hu, L. (2002). “Estimation of a Censored Dynamic Panel Data Model,” Econometrica, 70, 2499–2517.Google Scholar
Hurvich, C.M., and Tsai, C.L. (1989). “Regression and Time Series Model Selections in Small Samples,” Biometrika, 76, 297–307.Google Scholar
Hurwicz, L. (1950). “Systems with Nonadditive Disturbances,” in Statistical Inference in Dynamic Economic Models, edited by Koopmans, T.C., pp. 330–372, New York: Wiley.Google Scholar
Hyndman, R.J., Lee, A.J., and Wang, E. (2016). “Fast Computation of Reconciled Forecasts for Hierarchical and Grouped Time Series,” Computational Statistics and Data Analysis, 97, 16–32.Google Scholar
Hyslop, D. (1999). “State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women,” Econometrica, 52, 363–389.Google Scholar
Im, K.S., Lee, J., and Tieslau, M. (2005). “Panel LM Unit Root Tests with Level Shifts,” Oxford Bulletin of Economics and Statistics, 67, 393–419.Google Scholar
Im, K.S., Pesaran, M.H., and Shin, Y. (2003). “Testing for Unit Roots in Heterogeneous Panels,” Journal of Econometrics, 115, 53–74.Google Scholar
Imbens, G.W., and Angrist, J.D. (1994). “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62, 467–475.Google Scholar
Imbens, G.W., and Lemieux, T. (2008). “Regression Discontinuity Designs: A Guide to Practice,” Journal of Econometrics, 142, 615–635.Google Scholar
Inoue, A. (2008). “Efficient Estimation and Inference in Linear Pseudo-Panel Data Models,” Journal of Econometrics, 148, 449–466.Google Scholar
Intriligator, M.D., Bodkin, R.G., and Hsiao, C. (1996). Econometric Models, Techniques, and Applications, Upper Saddle River: Prentice-Hall.Google Scholar
Izan, H.Y. (1980). “To Pool or not to Pool? A Reexamination of Tobin's Food Demand Problem,” Journal of Econometrics, 13, 391–402.Google Scholar
Janz, N., Ebling, G., Gottshalk, S., and Niggemann, H. (2001). “The Mannheim Innovation Panels (MIP and MIP-S) of the Centre for European Economic Research (ZEW),” Schmollers Jahrbuch, 121, 123–129.Google Scholar
Jennrich, R.L., and Thayer, D.T. (1973). “A Note on Lawley's Formulas for Standard Errors in Maximum Likelihood Factor Analysis,” Psychometrica, 38, 571–580.Google Scholar
Jiang, B., Yang, Y., Gao, J., and Hsiao, C. (2020). “Recursive Estimation in Large Panel Data Models: Theory and Practice,” Journal of Econometrics, 224, 439–465.Google Scholar
Jin, F., and Lee, L.F. (2019). “GEL Estimation and Tests of Spatial Autoregressive Models,” Journal of Econometrics, 208, 585–612.Google Scholar
Johansen, S. (1991). “Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models,” Econometrica, 59, 1551–1580.Google Scholar
Johansen, S. (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, Oxford: Oxford University Press.Google Scholar
Jorgenson, D.W. (1971). “Econometric Studies of Investment Behavior: A Survey,” Journal of Economic Literature, 9, 1111–1147.Google Scholar
Ju, G., Gan, L., and Li, Q. (2019). “Nonparametric Panel Estimation of Labor Supply,” Journal of Business and Economic Statistics, 37, 260–274.Google Scholar
Judge, G., Griffiths, W., Hill, R., and Lee, T. (1980). The Theory and Practice of Econometrics, New York: Wiley.Google Scholar
Judson, R.A., and Owen, A.L. (1999). “Estimating Dynamic Panel Data Models: A Guide for Macroeconomists,” Economic Letters, 65, 9–15.Google Scholar
Juster, T. (2001). “Economics/Micro Data,” in International Encyclopedia of Social Sciences, edited by Snelser, N.J. and Bates, P.B., vol. 14, 9770–9777, Oxford: Elsevier.Google Scholar
Kalman, R.E. (1960). “A New Approach to Linear Filtering and Prediction Problems,” Transactions of the ASME, Series D., Journal of Basic Engineering, 82, 35–45.Google Scholar
Kao, C. (1999). “Spurious Regression and Residual-Based Tests for Cointegration in Panel Data,” Journal of Econometrics, 90, 1–44.Google Scholar
Kao, C., and Chiang, M.H. (2000). “On the Estimation and Inference of a Cointegrated Regression in Panel Data,” in Advances in Econometrics, vol. 15, edited by Baltagi, B., pp. 161–178, Amsterdam: JAI Press.Google Scholar
Kao, C., and Schnell, J.F. (1987a). “Errors in Variables in Panel Data with Binary Dependent Variable,” Economic Letters, 24, 45–49.Google Scholar
Kao, C., and Schnell, J.F. (1987b). “Errors-in-Variables in a Random Effects Probit Model for Panel Data,” Economic Letters, 24, 339–342.Google Scholar
Kapetanios, G., Pesaran, M.H., and Yamagata, T. (2011). “Panels with Non-Stationary Multifactor Error Structures,” Journal of Econometrics, 160, 326–348.Google Scholar
Kapetanios, G., Serlenga, L., and Shin, Y. (2020), “Testing Adequacy of the Fixed Effects Estimator in the Presence of Cross-Section Dependance,” mimeo.Google Scholar
Kapoor, M., Kelejian, H., and Prucha, I. (2007). “Panel Data Models with Spatially Correlated Error Components,” Journal of Econometrics, 140, 97–130.Google Scholar
Karlin, S., and Taylor, H. (1975). A First Course in Stochastic Processes, 2nd ed., New York: Academic Press.Google Scholar
Kato, K., Galvao, A.F., and Montes-Rojas, G.V. (2012). “Asymptotics for Panel Quantile Regression Models with Individual Effects,” Journal of Econometrics, 170, 76–91.Google Scholar
Ke, X., Chen, H., Hong, Y., and Hsiao, C. (2017). “Do China's High Speed Rail Projects Promote Local Economy? – New Evidence from a Panel Data Approach” China Economic Review, 44, 203–226.Google Scholar
Keane, M.P. (1994). “A Computationally Practical Simulation Estimator for Panel Data,” Econometrica, 62, 95–116.Google Scholar
Kelejian, H.H. (1977). “Random Parameters in Simultaneous Equation Framework: Identification and Estimation,” Econometrica, 42, 517–527.Google Scholar
Kelejian, H.H., and Prucha, I.R. (2001). “On the Asymptotic Distribution of the Moran I Test Statistic with Application,” Journal of Econometrics, 104, 219–257.Google Scholar
Kelejian, H.H., and Stephan, S.W. (1983). “Inference in Random Coefficient Panel Data Models: A Correction and Clarification of the Literature,” International Economic Review, 24, 249–254.Google Scholar
Kermack, W., and McKendrick, A. (1927). “A Contribution to the Mathematical Theory of Epidemics,” Proceedings of the Royal Society of London, Series A, 115, 700–721.Google Scholar
Kiefer, J., and Wolfowitz, J. (1956). “Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters,” Annals of Mathematical Statistics, 27, 887–906.Google Scholar
Kiefer, N.M. (1980). “Estimation of Fixed Effects Models for Time Series of Cross-Sections with Arbitrary Intertemporal Covariance,” Journal of Econometrics, 14, 195–202.Google Scholar
Kim, J., and Pollard, D. (1990). “Cube Root Asymptotics,” Annals of Statistics, 18, 191–219.Google Scholar
Kiviet, H.H. (1995). “On Bias Inconsistency and Efficiency in Various Estimators of Dynamic Panel Data Models,” Journal of Econometrics, 68, 53–78.Google Scholar
Kiviet, J.F., and Phillips, G.D.A. (1993). “Alternative Bias Approximations in Regressions with a Lagged-Dependent Variable,” Econometric Theory, 9, 62–80.Google Scholar
Klein, L.R. (1953). A Textbook of Econometrics, Evanston: Row Peterson.Google Scholar
Klein, L.R. (1988). “The Statistical Approach to Economics,” Journal of Econometrics, 37, 7–26.Google Scholar
Klein, L.R., and Goldberger, A. (1955). An Econometric Model of the United States, 1929–1952, Amsterdam: North Holland.Google Scholar
Klein, R., and Spady, R. (1993). “An Efficient Semiparametric Estimator for Binary Response Models,” Econometrica, 61(2), 387–423.Google Scholar
Koenker, R. (2004). “Quantile Regression for Longitudinal Data,” Journal of Multivariate Analysis, 91, 74–89.Google Scholar
Koenker, R., and Bassett, G. (1978). “Regression Quantiles,” Econometrica, 46, 33–50.Google Scholar
Koenker, R., and Machado, J.A.F. (1999). “GMM inference When the Number of Moment Condition Is Large,” Journal of Econometrics, 93, 327–344.Google Scholar
Kotchoni, R., Leroux, M., and Stevanovic, D. (2019). “Macroeconomic Forecast Accuracy in a Data-Rich Environment,” Journal of Applied Econometrics, 34(7), 1050–1072.Google Scholar
Kreider, R.B. (1991). “Physiological Considerations of Ultraendurance Performance,” International Journal of Sports Nutrition, 1, 3–27.Google Scholar
Kruiniger, H. (2009). “GMM Estimation and Inference in Dynamic Panel Data Models with Persistent Data,” Econometric Theory, 25, 1348–1391.Google Scholar
Kuh, E. (1963). Capital Stock Growth: A Micro-Econometric Approach, Amsterdam: North-Holland.Google Scholar
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P. and Shin, Y. (1992). “Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root,” Journal of Econometrics, 54, 159–178.Google Scholar
Kyriazidou, E. (1997). “Estimation of a Panel Data Sample Selection Model,” Econometrica, 65, 1335–1364.Google Scholar
Kyriazidou, E. (2001). “Estimation of Dynamic Panel Data Sample Selection Models,” Review of Economic Studies, 68, 543–572.Google Scholar
LaLonde, R. (1986). “Evaluating the Econometric Evaluations of Training Programs,” American Economic Review, 76, 604–620.Google Scholar
Lamarche, C. (2010). “Robust Penalized Quantile Regression Estimation for Panel Data,” Journal of Econometrics, 157, 396–408.Google Scholar
Lancaster, T. (1984). “The Covariance Matrix of the Information Matrix Test,” Econometrica, 52, 1051–1053.Google Scholar
Lancaster, T. (1990). The Econometric Analysis of Transition Data, New York: Cambridge University Press.Google Scholar
Lancaster, T. (2001). “Some Econometrics of Scarring,” in Nonlinear Statistical Inference, edited by Hsiao, C., Morimune, K., and Powell, J.L., pp. 393–402, New York: Cambridge University Press.Google Scholar
Larsson, R., Lyhagen, J., and Löthgren, M. (2001). “Likelihood-Based Cointegration Tests in Heterogeneous Panels,” Econometrics Journal, 4, 109–142.Google Scholar
Lawley, D.N. (1967). “Some New Results in Maximum Likelihood Factor Analysis,” Proceedings of the Royal Society of Edinburgh, Series A, 67, 256–264.Google Scholar
Layton, L. (1978). “Unemployment Over the Work History,” Ph.D. dissertation, Department of Economics, Columbia University.Google Scholar
Lee, K., and Pesaran, M.H. (1993). “Persistence Profiles and Business Cycle Fluctuations in a Disaggregated Model of UK Output Growth,” Ricerche Economiche, 47, 293–322.Google Scholar
Lee, L.F. (1982). “Specification Error in Multinominal Logit Models: Analysis of the Omitted Variable Bias,” Journal of Econometrics, 20, 197–209.Google Scholar
Lee, L.F. (1987). “Nonparametric Testing of Discrete Panel Data Models,” Journal of Econometrics, 34, 147–178.Google Scholar
Lee, L.F. (2002). “Consistency and Efficiency of Least Squares Estimation for Mixed Regressive, Spatial Autoregressive Models,” Econometric Theory, 18, 252–277.Google Scholar
Lee, L.F. (2003). “Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances,” Econometric Reviews, 22, 307–335.Google Scholar
Lee, L.F. (2004). “Asymptotic Distribution of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models,” Econometrica, 72, 1899–1925.Google Scholar
Lee, L.F., and Griffiths, W.E. (1979). “The Prior Likelihood and Best Linear Unbiased Prediction in Stochastic Coefficient Linear Models,” University of New England Working Papers in Econometrics and Applied Statistics, No. 1.Google Scholar
Lee, L.F., and Yu, J. (2010a). “Estimation of Spatial Autoregressive Panel Data Models with Fixed Effects,” Journal of Econometrics, 154, 165–185.Google Scholar
Lee, L.F., and Yu, J. (2010b). “A Spatial Dynamic Panel Data Model with Both Time and Individual Fixed Effects,” Econometric Theory, 26, 564–597.Google Scholar
Lee, M.J. (1999). “A Root-N Consistent Semiparametric Estimator for Related Effects Binary Response Panel Data,” Econometrica, 67, 427–433.Google Scholar
LeSage, J., and Pace, R.K. (2006). “A Matrix Exponential Spatial Specification,” Journal of Econometrics, 140 (2007), 190–214.Google Scholar
LeSage, J., and Pace, R.K. (2007). “Introduction to Spatial Econometrics,” Journal of Regional Science, 50(5), 1014–1015.Google Scholar
Levin, A., and Lin, C. (1993). “Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties,” mimeo, University of California, San Diego.Google Scholar
Levin, A., Lin, C., and Chu, J. (2002). “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties,” Journal of Econometrics, 108, 1–24.Google Scholar
Lewbel, A. (1992). “Aggregation and with Log Linear Models,” Review of Economic Studies, 59, 535–554.Google Scholar
Lewbel, A. (1994). “Aggregation and Simple Dynamics,” American Economic Review, 84, 905–918.Google Scholar
Li, D., Robinson, P.M., and Shang, H.L. (2018). “Long-Range Dependent Curve Time Series,” mimeo.Google Scholar
Li, K.T., and Bell, D. (2017). “Estimation of Average Treatment Effects with Panel Data: Asymptotic Theory and Implementation,” Journal of Econometrics, 197, 65–75.Google Scholar
Li, M., and Tobias, J.L. (2011). “Bayesian Inference in a Correlated Random Coefficients Model: Modeling Causal Effect Heterogeneity with an Application to Heterogeneous Returns to Schooling,” Journal of Econometrics, 162, 345–361.Google Scholar
Li, Q., and Hsiao, C. (1998). “Testing Serial Correlation in Semi-Parametric Panel Data Models,” Journal of Econometrics, 87, 207–237.Google Scholar
Li, Q., and Racine, J.S. (2007). Nonparametric Econometrics: Theory and Practice, Princeton: Princeton University Press.Google Scholar
Li, Q., and Stengos, T. (1996). “Semi-parametric Estimation of Partially Linear Panel Data Models,” Journal of Econometrics, 71, 389–397.Google Scholar
Liesenfeld, L., and Richard, J.F. (2008). “Simulation Techniques for Panels: Efficient Importance Sampling,” in The Econometrics of Panel Data, 3rd ed., edited by Mátyas, L. and Severstre, P., pp. 419–450, Berlin: Springer-Verlag.Google Scholar
Lillard, L.A., and Weiss, Y. (1979). “Components of Variation in Panel Earnings Data: American Scientists 1960–70,” Econometrica, 47, 437–454.Google Scholar
Lillard, L.A., and Willis, R. (1978). “Dynamic Aspects of Earnings Mobility,” Econometrica, 46, 985–1012.Google Scholar
Lin, C.C., and Ng, S. (2012). “Estimation of Panel Data Models with Parameter Heterogeneity When Group Membership Is Unknown,” Journal of Econometric Methods, 1, 42–55.Google Scholar
Lin, X., and Carroll, R.J. (2000). “Nonparametric Function Estimation for Clustered Data When the Predictor is Measured Without/With Error,” Journal of the American Statistical Association, 95, 520–534.Google Scholar
Lindley, D.V., and Smith, A.F.M. (1972). “Bayes Estimates for the Linear Model,” and Discussion, Journal of the Royal Statistical Society, Series B, 34, 1–41.Google Scholar
Little, R.J.A., and Rubin, D.B. (1987). Statistical Analysis with Missing Data, New York: Wiley.Google Scholar
Liu, E., Hsiao, C., Matsumoto, T., and Chou, S. (2009). “Maternal Full-Time Employment and Overweight Children: Parametric, Semi-Parametric and Non-parametric Assessment,” Journal of Econometrics, 152, 61–69.Google Scholar
Liu, T.C. (1960). “Underidentification, Structural Estimation, and Forecasting,” Econometrica, 28, 855–865.Google Scholar
Lu, X., Miao, K., and Su, L. (2020). “Determination of Different Types of Fixed Effects in Three-dimensional Panels,” Econometric Reviews, forthcoming.Google Scholar
Lu, X., and Su, L. (2020). “Determining Individual or Time Fixed Effects in Panel Data Models,” Journal of Econometrics, 215, 60–83.Google Scholar
Lucas, L. (1976). “Econometric Policy Evaluation: A Critique”, in Brumer, K. and Meltzer, A. (eds.), The Phillips Curve and Labor Markets. Carnegie-Rochester Conference Series on Public Policy 1, New York: Elsevier, pp. 19–46.Google Scholar
Ma, S., Racine, J.S., and Yang, L. (2015). “Spline Regression in the Presence of Categorical Predictors,” Journal of Applied Econometrics, 30, 703–717.Google Scholar
Maasoumi, E., and Wang, L. (2017). “What Can We Learn about the Racial Gap in the Presence of Sample Selection?” Journal of Econometrics, 199, 117–130.Google Scholar
Maasoumi, E., and Wang, L. (2019). “The Gender Gap between Earning Distributions,” Journal of Political Economy, 127, 2438–2504.Google Scholar
Maasoumi, E. and Wang, L. (2020). “Women's Counterfactual Earnings Distributions,” mimeo.Google Scholar
MaCurdy, T.E. (1981). “An Empirical Model of Labor Supply in a Life Cycle Setting,” Journal of Political Economy, 89, 1059–1085.Google Scholar
MaCurdy, T.E. (1982). “The Use of Time Series Processes to Model the Error Structure of Earnings in a Longitudinal Data Analysis,” Journal of Econometrics, 18, 83–114.Google Scholar
Maddala, G.S. (1971a). “The Use of Variance Components Models in Pooling Cross Section and Time Series Data,” Econometrica, 39, 341–358.Google Scholar
Maddala, G.S. (1971b). “The Likelihood Approach to Pooling Cross-Section and Time Series Data,” Econometrica, 39, 939–958.Google Scholar
Maddala, G.S. (1983). Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.Google Scholar
Maddala, G.S., and Mount, T.D. (1973). “A Comparative Study of Alternative Estimators for Variance Components Models Used in Econometric Applications,” Journal of the American Statistical Association, 68, 324–328.Google Scholar
Maddala, G.S., and Wu, S. (1999). “A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test,” Oxford Bulletin of Economics and Statistics, 61, 631–652.Google Scholar
Magnus, J.R., and Neudecker, H. (1999). Matrix Differential Calculus with Applications in Statistics and Econometrics, revised ed., New York: John Wiley and Sons.Google Scholar
Malinvaud, E. (1970). Statistical Methods of Econometrics, 2nd ed., Amsterdam: North-Holland.Google Scholar
Mankiw, N.G., Romer, D., and Weil, D. (1992). “A Contribution to the Empirics of Economic Growth,” Quarterly Journal of Economics, 107, 407–437.Google Scholar
Manski, C.F. (1975). “Maximum Score Estimation of the Stochastic Utility Model of Choice,” Journal of Econometrics, 3, 205–228.Google Scholar
Manski, C.F. (1985). “Semiparametric Analysis of Discrete Response: Asymptotic Properties of the Maximum Score Estimator,” Journal of Econometrics, 27, 313–333.Google Scholar
Manski, C.F. (1987). “Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data,” Econometrica, 55, 357–362.Google Scholar
Manski, C.F., and Tamer, E. (2002). “Inference on Regressions with Interval Data on a Regressor or Outcome,” Econometrica, 70, 519–546.Google Scholar
Mao, G., and Shen, Y. (2013). “Bubbles or Fundamentals? Modeling Provincial House Prices in China Allowing for Cross-Sectional Dependence,” mimeo, National School of Development, Peking University.Google Scholar
Martins-Filho, C., and Yao, F. (2009). “Nonparametric Regression Estimation with General Parametric Error Covariance,” Journal of Multivariate Analysis, 100, 309–333.Google Scholar
Matillion (2019). “User Guide to Machine Learning,” mimeo.Google Scholar
Matyas, L. (Ed.) (2017). The Econometrics of Multi-dimensional Panels: Theory and Applications, Springer, Switzerland.Google Scholar
Mazodier, P., and Trognon, A. (1978). “Heteroscedasticity and Stratification in Error Components Models,” Annales de l’INSEE, 30–31, 451–482.Google Scholar
McCullah, P. (1987). Tensor Methods in Statistics, London: Chapman and Hall.Google Scholar
McCoskey, S., and Kao, C. (1998). “A Residual-Based Test of the Null of Cointegration in Panel Data,” Econometric Reviews, 17, 57–84.Google Scholar
McFadden, D. (1976). “Quantal Choice Analysis: A Survey,” Annals of Economic and Social Measurement, 5, 363–390.Google Scholar
McFadden, D. (1984). “Econometric Analysis of Qualitative Response Models,” in Handbook of Econometrics, vol. II, edited by Griliches, Z. and Intriligator, M.D., pp. 1395–1457. Amsterdam: North-Holland.Google Scholar
McFadden, D. (1989). “A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration,” Econometrica, 57, 995–1026.Google Scholar
McKenzie, D.J. (2004). “Asymptotic Theory for Heterogeneous Dynamic Pseudo-Panels,” Journal of Econometrics, 120, 235–262.Google Scholar
Mehta, J.S., Narasimham, G.V.L., and Swamy, P.A.V.B. (1978). “Estimation of a Dynamic Demand Function for Gasoline with Different Schemes of Parameter Variation,” Journal of Econometrics, 7, 263–279.Google Scholar
Merton, R.C. (1974). “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” Journal of Finance, 29, 449–470.Google Scholar
Meyer, J.R., and Kuh, E. (1957). The Investment Decision: An Empirical Study. Cambridge: Harvard University Press.Google Scholar
Min, C.K., and Zellner, A. (1993). “Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rate,” Journal of Econometrics, 56, 89–118.Google Scholar
Modigliani, F., and Miller, M.H. (1958). “The Cost of Capital, Corporation Finance and the Theory of Investment,” American Economic Review, 48, 261–297.Google Scholar
Moffitt, R. (1993). “Identification and Estimation of Dynamic Models with a Time Series of Repeated Cross-Sections,” Journal of Econometrics, 59, 99–123.Google Scholar
Moon, H.R., and Perron, B. (2004). “Testing for a Unit Root in Panels with Dynamic Factors,” Journal of Econometrics, 122, 81–126.Google Scholar
Moon, H.R., and Weidner, M. (2015). “Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects,” Econometrica, 83(4), 1543–1579.Google Scholar
Moore, M.I. (1996). “Death and Tobacco Taxes,” Rand Journal of Economics, 27, 415–428.Google Scholar
Mullachery, V., Khera, A., and Husain, A. (2018), “Bayesian Neural Networks,” arXiv:1111.4248.Google Scholar
Mundlak, Y. (1961). “Empirical Production Function Free of Management Bias,” Journal of Farm Economics, 43, 44–56.Google Scholar
Mundlak, Y. (1978a). “On the Pooling of Time Series and Cross Section Data,” Econometrica, 46, 69–85.Google Scholar
Mundlak, Y. (1978b). “Models with Variable Coefficients: Integration and Extension,” Annales de l’INSEE, 30–31, 483–509.Google Scholar
Nagar, A.L. (1959). “The Bias and Moment Matrix of k-class Estimators of the Parameters in Simultaneous Equations,” Econometrica, 27, 575–595.Google Scholar
Nasreen, S., Saidi, S., and Ozturk, I. (2018). “Assesing Links between Energy Consumption, Freight Transport, and Economic Growth: Evidence from Dynamic Simultaneous Equation Models,” Environmental Science and Pollution Research, 25, 16825–16841Google Scholar
National Institute on Drug Abuse (2018). “Tobacco, Nicotine and E-Cigarettes,” online document, https://teens.drugabuse.gov//drug-facts/tobacco-nicotine-e-cigarettes.Google Scholar
Nerlove, M. (1965). Estimation and Identification of Cobb-Douglas Production Functions. Chicago: Rand McNally.Google Scholar
Nerlove, M. (1967). “Experimental Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross-Sections,” Economic Studies Quarterly, 18, 42–74.Google Scholar
Nerlove, M. (1971a). “Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections,” Econometrica, 39, 359–382.Google Scholar
Nerlove, M. (1971b). “A Note on Error Components Models,” Econometrica, 39, 383–396.Google Scholar
Nerlove, M. (2002). Essays in Panel Data Econometrics, Cambridge: Cambridge University Press.Google Scholar
Nevale, K. (2019). “SAS Best Practices E-book,” The Machine Learning Primer.Google Scholar
Newey, W.K. (1994). “The Asymptotic Variance of Semiparametric Estimators,” Econometrica, 62(6), 1349–1382.Google Scholar
Newey, W.K. (1997). “Convergence Rate and Asymptotic Normality for Series Estimators,” Journal of Econometrics, 79, 147–168.Google Scholar
Newey, W.K. (2009). “Two Step Series Estimation of Sample Selection Models,” The Econometrics Journal, 12, S217–S229.Google Scholar
Newey, W., and West, K. (1987). “A Simple Positive Semi-Definite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, 50, 703–708.Google Scholar
Neyman, J. (1949). “Contribution to the Theory of the χ2 Test,” in Proceedings of the First Berkeley Symposium on Mathematical Statistics and Probabilities, edited by Neyman, J., pp. 230–270, Berkeley, CA: University of California Press.Google Scholar
Neyman, J., and Scott, E.L. (1948). “Consistent Estimates Based on Partially Consistent Observations,” Econometrica, 16, 1–32.Google Scholar
Nickell, S. (1981). “Biases in Dynamic Models with Fixed Effects,” Econometrica, 49, 1399–1416.Google Scholar
Nijman, T.H.E., and Verbeek, M. (1992). “Nonresponse in Panel Data: The Impact on Estimates of a Life Cycle Consumption Function,” Journal of Applied Econometrics, 7, 243–257.Google Scholar
Nijman, T.H.E., Verbeek, M., and van Soest, A. (1991). “The Efficiency of Rotating Panel Designs in an Analysis of Variance Model,” Journal of Econometrics, 49, 373–399.Google Scholar
Okui, R. (2009). “The Optimal Choice of Moments in Dynamic Panel Data Models,” Journal of Econometrics, 151, 1–16.Google Scholar
Ord, J.K. (1975). “Estimation Methods for Models of Spatial Interaction,” Journal of the American Statistical Association, 70, 120–126.Google Scholar
Pagan, A. (1980). “Some Identification and Estimation Results for Regression Models with Stochastically Varying Coefficients,” Journal of Econometrics, 13, 341–364.Google Scholar
Pakes, A., and Griliches, Z. (1984). “Estimating Distributed Lags in Short Panels with an Application to the Specification of Depreciation Patterns and Capital Stock Constructs,” Review of Economic Studies, 51, 243–262.Google Scholar
Pakes, A., and Pollard, D. (1989). “Simulation and the Asymptotics of Optimization Estimators,” Econometrica, 57, 1027–1057.Google Scholar
Parmeter, C., and Racine, J. (2018). “Nonparametric Estimation and Inference for Panel Data,” mimeo.Google Scholar
Pedroni, P. (1994). “Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis,” Econometric Theory, 20, 597–625.Google Scholar
Pedroni, P. (2004). “Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis,” Econometric Theory, 20, 597–625.Google Scholar
Peracchi, F. (2000). “The European Community Household Panel: A Review,” paper presented at the Panel Data Conference in Geneva.Google Scholar
Perron, P. (1989). “The Great Crash, the Oil Price Shock and the Unit Root Hypothesis,” Econometrica, 57, 1361–1401.Google Scholar
Pesaran, M.H. (2003). “On Aggregation of Linear Dynamic Models: An Application to Life-Cycle Consumption Models Under Habit Formation,” Economic Modeling, 20, 227–435.Google Scholar
Pesaran, M.H. (2006). “Estimation and Inference in Large Heterogeneous Panels with Cross-Section Dependence,” Econometrica, 74, 967–1012.Google Scholar
Pesaran, M.H. (2007). “A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence,” Journal of Applied Econometrics, 22, 265–312.Google Scholar
Pesaran, M.H. (2012). “On the Interpretation of Panel Unit Root Tests,” Economics Letters, 116, 545–546.Google Scholar
Pesaran, M.H. (2020). “General Diagnostic Tests for Cross-Section Dependence in Panels,” Empirical Economics, 60, 13–50.Google Scholar
Pesaran, M.H., and Pick, A. (2011). “Forecast Combination across Estimation Windows,” Journal of Business Economics and Statistics, 29, 307–318.Google Scholar
Pesaran, M.H., and Smith, R. (1995). “Estimation of Long-Run Relationships from Dynamic Heterogeneous Panels,” Journal of Econometrics, 68, 79–114.Google Scholar
Pesaran, M.H., and Tosetti, E. (2011). “Large Panels with Common Factors and Spatial Correlations,” Journal of Econometrics, 161, 182–202.Google Scholar
Pesaran, M.H., and Yamagata, T. (2008). “Testing Slope Homogeneity in Large Panels,” Journal of Econometrica, 142, 50–93.Google Scholar
Pesaran, M.H. and Yang, C.F. (2020), “Econometric Networks with Dominant Units,” Journal of Econometrics, 219, 507–541.Google Scholar
Pesaran, M.H., and Yang, C.F. (2021). “Matching Theory and Evidence on COVID-19 using a Stochastic Network SIR Model,” mimeo.Google Scholar
Pesaran, M.H., and Zhao, Z. (1999). “Bias Reduction in Estimating Long-Run Relationships from Dynamic Heterogeneous Panels,” in Analysis of Panels and Limited Dependent Variables, edited by Hsiao, C., Lahiri, K., Lee, L.F., and Pesaran, M.H., pp. 297–322, Cambridge: Cambridge University Press.Google Scholar
Pesaran, M.H., Pierse, R., and Lee, L. (1994). “Choice between Disaggregate and Aggregate Specifications Estimated by Instrumental Variables Methods,” Journal of Business & Economic Statistics, 12, 11–21.Google Scholar
Pesaran, M.H., Schuermann, T. and Weiner, S.M. (2004). “Modelling Regional Interdependencies Using a Global Error-Correction Macroeconometrics Model,” Journal of Business and Economic Statistics, 22, 129–162.Google Scholar
Pesaran, M.H., Shin, Y., and Smith, R.J. (1999). “Pooled Mean Group Estimation of Dynamic Heterogeneous Panels,” Journal of the American Statistical Association, 94, 621–634.Google Scholar
Pesaran, M.H., Smith, L.V., and Yamagata, T. (2013). “Panel Unit Root Tests in the Presence of a Multifactor Error Structure,” Journal of Econometrics, 175, 94–115.Google Scholar
Pesaran, M.H., Ullah, A., and Yamagata, T. (2008). “A Bias Adjusted LM Test of Error Cross-Section Independence,” Econometrics Journal, 11, 105–127.Google Scholar
Phelps, E. (1972). Inflation Policy and Unemployment Theory: The Cost Benefit Approach to Monetary Planning, London: Macmillan.Google Scholar
Phillips, P.C.B. (1974). “The Estimation of Some Continuous Time Models,” Econometrica, 42, 803–823.Google Scholar
Phillips, P.C.B. (1986). “Understanding Spurious Regressions in Econometrics,” Journal of Econometrics, 33, 311–340.Google Scholar
Phillips, P.C.B. (1991). “Optimal Inference in Cointegrated Systems,” Econometrica, 59, 283–306.Google Scholar
Phillips, P.C.B., and Durlauf, S.N. (1986). “Multiple Time Series Regression with Integrated Processes,” Review of Economic Studies, 53, 473–495.Google Scholar
Phillips, P.C.B., and Hansen, B.E. (1990). “Statistical Inference in Instrumental Variables Regression with I(1) Processes,” Review of Economic Studies, 57, 99–125.Google Scholar
Phillips, P.C.B., and Moon, H.R. (1999). “Linear Regression Limit Theory for Nonstationary Panel Data,” Econometrica, 67, 1057–1111.Google Scholar
Phillips, P.C.B., and Moon, H.R. (2000). “Nonstationary Panel Data Analysis: An Overview of Some Recent Developments,” Econometrics Review, 19, 263–286.Google Scholar
Phillips, P.C.B., and Sul, D. (2003). “Dynamic Panel Estimation and Homogeneity Testing Under Cross Section Dependence,” The Econometrics Journal, 6, 217–259.Google Scholar
Phillips, P.C.B., and Moon, H.R. (2007). “Bias in Dynamic Panel Estimation with Fixed Effects, Incidental Trends and Cross-Section Dependence,” Journal of Econometrics, 137, 162–188.Google Scholar
Powell, J.L. (1984). “Least Absolute Deviations Estimation for the Censored Regression Model,” Journal of Econometrics, 25, 303–325.Google Scholar
Powell, J.L. (1986). “Symmetrically Trimmed Least Squares Estimation for Tobit Models,” Econometrica, 54, 1435–1460.Google Scholar
Powell, J.L., Stock, J., and Stoker, T. (1989). “Semiparametric Estimation of Index Coefficients,” Econometrica, 57, 1403–1430.Google Scholar
Priestley, M.B. (1982). Spectral Analysis and Time Series, Vols I and II, New York: Academic Press.Google Scholar
Prucha, I.R. (1983). “Maximum Likelihood and Instrumental Variable Estimation in Simultaneous Equation Systems with Error Components,” working paper No. 83–6, Department of Economics, University of Maryland.Google Scholar
Qu, X., and Lee, L.F. (2015). “Estimating a Spatial Autoregressive Model with an Endogenous Spatial Weight Matrix,” Journal of Econometrics, 184, 209–232.Google Scholar
Quah, D. (1994). “Exploiting Cross-Section Variations for Unit Root Inference in Dynamic Data,” Economic Letters, 44, 9–19.Google Scholar
Quandt, R.E. (1982). “Econometric Disequilibrium Models,” Econometric Reviews, 1, 1–64.Google Scholar
Raj, B., and Ullah, A. (1981). Econometrics, A Varying Coefficient Approach, London: Croom Helm.Google Scholar
Ranco, G., Aleksovski, D., Caldarelli, G., Gr×car, M., and Moset×c, I. (2015). “The Effects of Twitter Sentiment on Stock Price Returns,” HYPERLINK “about:blank” https://doi.org/10.1371/journal.pone.0138441.Google Scholar
Rao, C.R. (1952). Advanced Statistical Methods in Biometric Research, New York: Wiley.Google Scholar
Rao, C.R. (1970). “Estimation of Heteroscedastic Variances in Linear Models,” Journal of the American Statistical Association, 65, 161–172.Google Scholar
Rao, C.R. (1972). “Estimation of Variance and Covariance Components in Linear Models,” Journal of the American Statistical Association, 67, 112–115.Google Scholar
Rao, C.R. (1973). Linear Statistical Inference and Its Applications, 2nd ed., New York: Wiley.Google Scholar
Richard, J.F. (1996). “Simulation Techniques,” in The Econometrics of Panel Data, 2nd ed., edited by Matyas, L. and Sevestre, P., pp. 613–638, Dordrecht: Kluwer Academic Publishers.Google Scholar
Richard, J.F., and Zhang, W. (2007). “Efficient High-Dimensional Importance Sampling,” Journal of Econometrics, 141, 1385–1411.Google Scholar
Ridder, G. (1990). “Attrition in Multi-Wave Panel Data,” in Panel Data and Labor Market Studies, edited by Hartog, J., Ridder, G., and Theeuwes, J., pp. 45–79, Amsterdam: North-Holland.Google Scholar
Ridder, G. (1992). “An Empirical Evaluation of Some Models for Non-random Attrition in Panel Data,” Structural Change and Economic Dynamics, 3, 337–335.Google Scholar
Robertson, D., and Sarafidis, V. (2015). “IV Estimation of Panels with Factor Residuals,” Journal of Econometrics, 185, 526–541.Google Scholar
Robinson, P.M. (1976). “The Estimatiion of Linear Differential Equations with Constant Coefficients,” Econometrica, 44, 751–763.Google Scholar
Robinson, P.M. (1988a). “Semiparametric Econometrics: A Survey,” Journal of Applied Econometrics, 3, 35–51.Google Scholar
Robinson, P.M. (1988b). “Root-N-Consistent Semiparametric Regression,” Econometrica, 56, 931–954.Google Scholar
Robinson, P.M. (1989). “Notes on Nonparametric and Semiparametric Estimation,” mimeo, London School of Economics.Google Scholar
Rosen, S. (1977). “Comment,” Industrial and Labor Relations Review, 30, 518.Google Scholar
Rosenberg, B. (1972). “The Estimation of Stationary Stochastic Regression Parameters Reexamined,” Journal of the American Statistical Association, 67, 650–654.Google Scholar
(1973). “The Analysis of a Cross-Section of Time Series by Stochastically Convergent Parameter Regression,” Annals of Economic and Social Measurement, 2 399–428.Google Scholar
Rosenbaum, P.R., and Rubin, D.B. (1983). “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” Biometrika, 70, 41–55.Google Scholar
Rosenbaum, P.R., and Rubin, D.B. (1985). “Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score,” The American Statistician, 39, 33–38.Google Scholar
Rothenberg, T.J. (1973). Efficient Estimation with a Priori Information. New Haven: Yale University Press.Google Scholar
Rubin, D.B. (1976). “Inference and Missing Data,” Biometrica, 63, 581–592.Google Scholar
Ruckstuhl, A.F., Welsh, A.H., and Carroll, R.J. (2000). “Nonparametric Function Estimation of the Relationship Between Two Repeatedly Measured Variables”, Statistica Sinica, 10, 51–71.Google Scholar
Saikkonen, P. (1991). “Asymptotically Efficient Estimation of Cointegration Regressions,” Econometric Theory, 7, 1–21.Google Scholar
Sant, D. (1977). “Generalized Least Squares Applied to Time-Varying Parameter Models,” Annals of Economic and Social Measurement, 6, 301–314.Google Scholar
Sarafidis, V., and Wansbeek, T. (2012). “Cross-Sectional Dependence in Panel Data Analysis,” Econometric Reviews, 31, 483–531.Google Scholar
Sarafidis, V., Yamagata, T., and Robertson, D. (2009). “A Test of Cross-Section Dependence for a Linear Dynamic Panel Model with Regressors,” Journal of Econometrics, 148, 149–161.Google Scholar
Sargan, J.D. (1958). “The Estimation of Economic Relationships Using Instrumental Variables,” Econometrica, 26, 393–415.Google Scholar
Sargan, J.D., and Bhargava, A. (1983). “Testing for Residuals from Least Squares Regression Being Generated by Gaussian Random Walk,” Econometrica, 51, 153–174.Google Scholar
Sbordone, A., Tambalotti, A., Rao, K., and Walsh, K. (2010). “Policy Analysis Using DSGE Models: An Introduction,” FRBNY Economic Policy Review, 16.Google Scholar
Schmidt, P. (1984). “Simultaneous Equation Models with Fixed Effects,” mimeo, Michigan State University.Google Scholar
Schwarz, G. (1978). “Estimating the Dimension of a Model,” Annals of Statistics, 6, 461–464.Google Scholar
Serfling, R.J. (1982), “Approximation Theorems of Mathematical Statistics,” in Wiley Series in Probability and Statistics.Google Scholar
Sevestre, P., and Trognon, A. (1982). “A Note on Autoregressive Error Component Models,” #8204, Ecole Nationale de la Statistique et de l’Administration Economique et Unite de Recherche.Google Scholar
Shapley, L.S., and Shubik, M. (1972). “The Assignment Game, I, The Core,” International Journal of Game Theory, 1, 111–130.Google Scholar
Sharifraghefi, M. (2020). “Essays on Variable Selection Problem in Linear Regression with Many Covariates,” Ph.D. dissertation, University of Southern California.Google Scholar
Sheiner, L., Rosenberg, B., and Melmon, K. (1972). “Modeling of Individual Pharmacokinetics for Computer-Aided Drug Dosage,” Computers and Biomedical Research, 5, 441–459.Google Scholar
Shen, X. (1997). “On Methods of Sieves and Penalization,” Annals of Statistics, 25, 2555–2591.Google Scholar
Sims, C. (1980). “Macroeconomic and Reality,” Econometrica, 48, 1–48.Google Scholar
Sims, C., Stock, J.H., and Watson, M.W. (1990). “Inference in Linear Time Series Models with Some Unit Roots,” Econometrica, 58(1), 113–144.Google Scholar
Singer, B., and Spilerman, S. (1976). “Some Methodological Issues in the Analysis of Longitudinal Surveys,”Annals of Economic and Social Measurement, 5, 447–474.Google Scholar
Singh, B., Nagar, A.L., Choudhry, N.K., and Raj, B. (1976). “On the Estimation of Structural Changes: A Generalization of the Random Coefficients Regression Model,” International Economic Review, 17, 340–361.Google Scholar
Small, K., and Hsiao, C. (1985). “Multinominal Logit Specification Tests,” International Economic Review, 26, 619–627.Google Scholar
Smith, A.F.M. (1973). “A General Bayesian Linear Model,” Journal of the Royal Statistical Society, Series B, 35, 67–75.Google Scholar
Solon, G. (1985). “Comment on ‘Benefits and Limitations of Data’ by C. Hsiao,” Econometric Reviews, 4, 183–186.Google Scholar
Stiglitz, J.E., and Weiss, A. (1981). “Credit Rationing in Markets with Imperfect Information,” American Economic Review, 71, 393–410.Google Scholar
Stock, J., and Watson, M.W. (2008). “Heteroskedasticity-Robust Standard Errors for Fixed Effects Regression,” Econometrica, 76, 155–174.Google Scholar
Stoker, T.M. (1993). “Empirical Approaches to the Problem of Aggregation over Individuals,” Journal of Economic Literature, 31, 1827–1874.Google Scholar
Stroud, A.H., and Secrest, D. (1966). Gaussian Quadrature Formulas, Englewood, NJ: Prentice Hall.Google Scholar
Su, L., and Ullah, A. (2011). “Nonparametric and Semiparametric Panel Economeetric Models: Estimation and Testing,” in Handbook of Empirical Economics and Finance, edited by Ullah, A. and Giles, D.E.A., New York: Taylor and Francis Group, 455–497.Google Scholar
Su, L., Shi, Z., and Phillips, P.C.B. (2016). “Identifying Latent Structures in Panel Data,” Econometrica, 84, 2215–2264.Google Scholar
Summers, L.H. (1981). “Taxation and Corporate Investment: A q-theory Approach,” Brookings Papers on Economic Activity, 1, 67–127.Google Scholar
Sun, Y., Hong, Y., Lee, T., Wang, S. and Zhang, X. (n.d.), “Time-Varying Model Averaging,” Journal of Econometrics, forthcoming, https://faculty.ucr.edu/~taelee/paper/2020%20JoE%20TJMA.pdfGoogle Scholar
Sun, Y., Hong, Y.M., and Wang, S.Y. (2018). “Time-Varying Model Averaging,” paper presented at the 2nd Annual Econometrics Forum, University of the Chinese Academy of Sciences, Beijing.Google Scholar
Sun, Y., Hong, Y.M., and Wang, S.Y. (2019). “Optimal Averaging for Interval Valued Data,” mimeo.Google Scholar
Sun, Y., Hong, Y.M., Wang, S.Y., and Zhang, X. (2020). “Time-Varying Model Averaging via Adaptive LASSO,” mimeo.Google Scholar
Swamy, P.A.V.B. (1970). “Efficient Inference in a Random Coefficient Regression Model,” Econometrica, 38, 311–323.Google Scholar
Swamy, P.A.V.B. (1971). “Statistical Inference in Random Coefficient Regression Models,” Berlin: Springer-Verlag.Google Scholar
Swamy, P.A.V.B., and Mehta, J.S. (1977). “Estimation of Linear Models with Time and Cross-Sectionally Varying Coefficients,” Journal of the American Statistical Association, 72, 890–898.Google Scholar
Swamy, P.A.V.B., and Tinsley, P.A. (1977). “Linear Prediction and Estimation Method for Regression Models with Stationary Stochastic Coefficients,” Special studies paper No. 78, Federal Reserve Board Division of Research and Statistics, Washington, DC.Google Scholar
Taub, A.J. (1979). “Prediction in the Context of the Variance-Components Model,” Journal of Econometrics, 10, 103–107.Google Scholar
Taylor, W.E. (1980). “Small Sample Consideration in Estimation from Panel Data,” Journal of Econometrics, 13, 203–223.Google Scholar
Temple, J. (1999). “The New Growth Evidence,” Journal of Economic Literature, 37(1), 112–156.Google Scholar
Theil, H. (1954). Linear Aggregation of Economic Relations, Amsterdam: North-Holland.Google Scholar
Theil, H. (1958). Economic Forecasts and Policy, Amsterdam: North-Holland.Google Scholar
Theil, H. (1971). Principles of Econometrics, New York: Wiley.Google Scholar
Theil, H., and Mennes, L.B.M. (1959). “Conception Stochastique de Coefficients Multiplicateurs dans l’Adjustment Lineaire des Series Temporelles,” Publications de l’Institut de Statistique de l’Universite de Paris, 8, 211–227.Google Scholar
Thieme, H.R. (2013), “Mathematics in Population Biology,” in Volume 12 of Princeton Series in Theoretical and Computational Biology, Princeton: Princeton University Press.Google Scholar
Tibshirani, R.J. (1996). “Regression Shrinkage and Selection via the LASSO,” Journal of the Royal Statistical Society, Series B, 58, 267–288.Google Scholar
Timmermann, A. (2006). “Forecast Combinations,” Chapter 4 in Handbook of Economic Forecasting vol. 1, 135–196, ed. by Elliott, G., Granger, C.W.J., and Timmermann, A., Elsevier.Google Scholar
Tobin, J. (1950). “A Statistical Demand Function for Food in the U.S.A.,” Journal of the Royal Statistical Society, Series A, 113, 113–141.Google Scholar
Tobin, J. (1958). “Estimation of Relationships for Limited Dependent Variables,” Econometrica, 26, 24–36.Google Scholar
Tobin, J. (1969). “A General Equilibrium Approach to Monetary Policy,” Journal of Money, Credit and Banking, 1, 15–29.Google Scholar
Trivedi, P.K. (1985). “Distributed Lags, Aggregation and Compounding: Some Econometric Implciations,” Review of Economic Studies, 52, 19–35.Google Scholar
Trognon, A. (1978). “Miscellaneous Asymptotic Properties of Ordinary Least Squares and Maximum Likelihood Estimators in Dynamic Error Components Models,” Annales de L’INSEE, 30–31, 631–657.Google Scholar
Tsiatis, A.A. (1981). “A Large Sample Study of Cox's Regression Model,” The Annals of Statistics, 9, 93–108.Google Scholar
Tu, D., and Ping, C. (1989). “Bootstrapping the Untrimmed L-Statistics,” Journal of Systems Science and Complexity, 9, 14–23.Google Scholar
Ullah, A., and Roy, N. (1998). “Nonparametric and Semiparametric Econometrics of Panel Data,” in Handbook of Applied Statistics, ed. by Ullah, A. and Giles, D.E.A., 579–604, New York: Marcel Dekker.Google Scholar
Van den Berg, G.J. (2001). “Duration Models: Specification, Identification and Multiple Durations,” Handbook of Econometrics, 5, 3381–3460.Google Scholar
Van Garderen, K.J., Lee, K., and Pesaran, M.H. (2000). “Cross-Sectional Aggregation of Non-Linear Models,” Journal of Econometrics, 95, 285–331.Google Scholar
Varian, H.R. (2014). “Big Data: New Tricks for Econometrics,” The Journal of Economic Perspective, 28(2), pp. 3–27, 2020.Google Scholar
Vella, F., and Verbeek, M. (1999). “Two-Step Estimation of Panel Data Models with Censored Endogenous Variables and Selection Bias,” Journal of Econometrics, 90, 239–264.Google Scholar
Verbeek, M. (2007). “Pseudo-Panels and Repeated Cross-Sections,” in The Econometrics of Panel Data, 3rd ed., edited by Matyas, L. and Severstre, P., pp. 369–384. Berlin: Springer-Verlag.Google Scholar
Verbeek, M., and Nijman, T.H.E. (1996). “Incomplete Panels and Selection Bias,” in Econometrics of Panel Data, 2nd ed., edited by Matyas, L. and Sevester, P., pp. 449–490, Dordercht: Kluwer Academic Publishers.Google Scholar
Verbeek, M., and Vella, F. (2005). “Estimating Dynamic Models from Repeated Cross-Sections,” Journal of Econometrics, 127, 83–102.Google Scholar
Vogelsang, T. (2012). “Heteroscedasticity, Autocorrelation and Spatial Correlation Robust Inference in Linear Panel Models with Fixed Effects,” Journal of Econometrics, 166, 303–319.Google Scholar
Wager, S., and Athey, S. (2018). “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests,” Journal of the American Statistical Association, 113, 1228–1242.Google Scholar
Wachter, M.L. (1970). “Relative Wage Equations for U.S. Manufacturing Industries 1947–1967,” Review of Economics and Statistics, 52, 405–410.Google Scholar
Wallace, T.D., and Hussain, A. (1969). “The Use of Error Components Models in Combining Cross-Section with Time Series Data,” Econometrica, 37, 55–72.Google Scholar
Wan, S.-K., Xie, Y., and Hsiao, C. (2018). “Panel Data Approach vs. Synthetic Control Method,” Economics Letters, 164, 121–123.Google Scholar
Wang, J. (2020). “Essays on the Estimation and Inference of Heterogenous Treatment Effects,” Ph.D. dissertation, University of Southern California.Google Scholar
Wang, J., and Huang, Y. (2019). “Nonparametric Estimation of Price Elasticities: A Heterogenous Treatment Effect Approach,” mimeo.Google Scholar
Wang, S., Bauwens, L., and Hsiao, C. (2013). “Forecasting Long Memory Processes Subject to Structural Breaks,” Journal of Econometrics, 177, 171–184.Google Scholar
Wansbeek, T.J. (1978). “The Separation of Individual Variation and Systematic Change in the Analysis of Panel Data,” Annales de I’INSEE, 30–31, 659–680.Google Scholar
Wansbeek, T.J. (1982). “A Class of Decompositions of the Variance–Covariance Matrix of a Generalized Error Components Model,” Econometrica, 50, 713–724.Google Scholar
Wansbeek, T.J. (2001). “GMM Estimation in Panel Data Models with Measurement Error,” Journal of Econometrics, 104, 259–268.Google Scholar
Wansbeek, T.J., and Bekker, P.A. (1996). “On IV, GMM and ML in a Dynamic Panel Data Model,” Economic Letters, 51, 145–152.Google Scholar
Wansbeek, T.J., and Kapteyn, A. (1978). “The Separation of Individual Variation and Systematic Change in the Analysis of Panel Data,” Annales de I’INSEE, 30–31, 659–680.Google Scholar
Wansbeek, T.J., and Koning, R.H. (1989). “Measurement Error and Panel Data,” Statistica Neerlandica, 45, 85–92.Google Scholar
Wansbeek, T.J., and Koning, R.H. (2000). Measurement Error and Latent Variables in Econometrics, Amsterdam: Elsevier.Google Scholar
Wansbeek, T.J., and Koning, R.H. (2007). “Comment on Panel Data Analysis – Advantages and Challenges,” TEST, 16, 33–36.Google Scholar
Westerlund, J. (2005). “New Simple Tests for Panel Cointegration,” Econometric Reviews, 24, 297–231.Google Scholar
Westerlund, J., and Larsson, R. (2012). “Testing for a Unit Root in Random Coefficient Panel Data Model,” Journal of Econometrics, 167, 254–273.Google Scholar
Westerlund, J., and Urbain, J.P. (2013). “Cross-Sectional Averages or Principal Components?”, mimeo, Maastricht University.Google Scholar
White, H. (1980). “A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity,” Econometrica, 48, 817–38.Google Scholar
White, H. (1989). “Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models,” Journal of the American Statistical Association, 84, 1003–1013.Google Scholar
White, H. (2001). Asymptotic Theory for Econometricians, Revised edition, Bingley: Emerald Publishing.Google Scholar
Wickramasuriya, S.L., Athanasopoulos, G., and Hyndman, R.J. (2019). “Optimal Forecast Reconciliation for Hierarchical and Grouped Trace Minimization,” Journal of the American Statistical Association, 114(526), 804–819.Google Scholar
Wilhelm, D. (2015). “Identification and Estimation of Nonparametric Panel Data Regressions with Measurement Error,” University College London working paper.Google Scholar
Wooldridge, J.M. (1999). “Distribution-Free Estimation of Some Nonlinear Panel Data Models,” Journal of Econometrics, 90, 77–98.Google Scholar
Wright, B.D., and Douglas, G. (1976). “Better Procedures for Sample-Free Item Analysis,” Research Memorandum 20, Statistical Laboratory, Department of Education, University of Chicago.Google Scholar
Xu, Y. (2017). Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Model, Political Analysis, 25(1), 57–76.Google Scholar
Yu, J., and Lee, L.F. (2010). “Estimation of Unit Root Spatial Dynamic Panel Data Models,” Econometric Theory, 26, 1332–1362.Google Scholar
Yu, J., de Jong, R., and Lee, L.F. (2012). “Estimation for Spatial Dynamic Panel Data with Fixed Effects: The Case of Spatial Cointegration,” Journal of Econometrics, 167, 16–37.Google Scholar
Zacks, S. (1971). The Theory of Statistical Inference, New York: Wiley.Google Scholar
Zellner, A. (1962). “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias,” Journal of the American Statistical Association, 57, 348–368.Google Scholar
Zellner, A. (1966). “On the Aggregation Problem: A New Approach to a Troublesome Problem,” in Economic Models, Estimation and Risk Programming: Essays in Honor of Gerhard Tintner, edited by Fox, K., pp. 365–374. Berlin: Spinger-Verlag.Google Scholar
Zellner, A. (1970). “Estimation of Regression Relationships Containing Unobservable Variables,” International Economic Review, 11, 441–454.Google Scholar
Zellner, A., and Theil, H. (1962). “Three Stage Least Squares: Simultaneous Estimation of Simultaneous Equations,” Econometrica, 30, 54–78.Google Scholar
Zellner, A., Hong, C., and Min, C.K. (1991). Forecasting Turning Points in International Output Growth Rates Using Bayesian Exponentially Weighted Autoregression, Time Varying Parameter and Pooling Techniques,” Journal of Econometrics, 49, 275–304.Google Scholar
Ziliak, J.P. (1997). “Efficient Estimation with Panel Data When Instruments Are Predetermined: An Empirical Comparison of Moment-Condition Estimators,” Journal of Business and Economic Statistics, 15, 419–431.Google Scholar
Zou, H., and Hastie, T. (2005). “Regularization and Variable Selection via the Elastic Net,” Journal of Royal Statistical Society, B., 67, 301–320.Google Scholar

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  • References
  • Cheng Hsiao, University of Southern California
  • Book: Analysis of Panel Data
  • Online publication: 19 May 2022
  • Chapter DOI: https://doi.org/10.1017/9781009057745.016
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  • References
  • Cheng Hsiao, University of Southern California
  • Book: Analysis of Panel Data
  • Online publication: 19 May 2022
  • Chapter DOI: https://doi.org/10.1017/9781009057745.016
Available formats
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  • References
  • Cheng Hsiao, University of Southern California
  • Book: Analysis of Panel Data
  • Online publication: 19 May 2022
  • Chapter DOI: https://doi.org/10.1017/9781009057745.016
Available formats
×