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AUTOMATED DISCOVERY IN ECONOMETRICS

Published online by Cambridge University Press:  08 February 2005

Peter C.B. Phillips
Peter C.B. Phillips
Affiliation:
Cowles Foundation, Yale University University of Auckland and University of York
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Abstract

Our subject is the notion of automated discovery in econometrics. Advances in computer power, electronic communication, and data collection processes have all changed the way econometrics is conducted. These advances have helped to elevate the status of empirical research within the economics profession in recent years, and they now open up new possibilities for empirical econometric practice. Of particular significance is the ability to build econometric models in an automated way according to an algorithm of decision rules that allow for (what we call here) heteroskedastic and autocorrelation robust (HAR) inference. Computerized search algorithms may be implemented to seek out suitable models, thousands of regressions and model evaluations may be performed in seconds, statistical inference may be automated according to the properties of the data, and policy decisions can be made and adjusted in real time with the arrival of new data. We discuss some aspects and implications of these exciting, emergent trends in econometrics.The first version of this paper was written in April 2004 for the 20th Anniversary Issue of Econometric Theory. Helpful comments by the co-editor, Oliver Linton, Benno Pötscher, Brendan Beare, and two referees on the first draft are gratefully acknowledged.

Type
Research Article
Information
Econometric Theory , Volume 21 , Issue 1 , February 2005 , pp. 3 - 20
Copyright
© 2005 Cambridge University Press

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References

REFERENCES

Bai, J. & S. Ng (2001) Determining the number of factors in approximate factor models. Econometrica 70, 191–223.Google Scholar
Bauer, D. (2004) Subspace Algorithms. Mimeo, TU Wien.
Bauer, D. (2005) Estimating linear dynamical systems using subspace methods. Econometric Theory (this issue).Google Scholar
Bauer, D. & M. Wagner (2002) Estimating cointegrated systems using subspace algorithms. Journal of Econometrics 111, 47–84.Google Scholar
Bauer, D. & M. Wagner (2003) Canonical Form for Unit Root Processes in the State Space Framework. Mimeo, TU Wien.
Cartwright, N. (1999) The Dappled World: A Study of the Boundaries of Science. Cambridge University Press.
Chrystal, K.A. & P.D. Mizen (2001) Goodhart's law: Its origins, meaning and implications for monetary policy. In Paul Mizen (ed.), Central Banking, Monetary Theory and Practice: Essays in Honour of Charles Goodhart, vol. 1, pp. 221–243. Edward Elgar.
Danilov, D.L. & J.R. Magnus (2004a) On the harm that ignoring pretesting can cause. Journal of Econometrics 122, 27–46.Google Scholar
Danilov, D.L. & J.R. Magnus (2004b) Forecast accuracy after pretesting with an application to the stock market. Journal of Forecasting (forthcoming).Google Scholar
Den Haan, W.J. & A. Levin (1997) A practitioner's guide to robust covariance matrix estimation. In G.S. Maddala & C.R. Rao (eds.), Handbook of Statistics, 15th ed., pp. 299–342. Elsevier.
Driscoll, J.C. & A.C. Kraay (1998) Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics 80, 549–560.Google Scholar
Duchesne, P. (2003) On Testing for Serial Correlation with a Wavelet-Based Spectral Density Estimator in Multivariate Time Series. Working paper, University of Montreal.
Glymour, C. (2004) The automation of discovery. Daedalus (winter), 69–77.Google Scholar
Goodhart, C.A.E. (1975) Monetary relationships: A view from Threadneedle Street. In Papers in Monetary Economics, vol. 1. Reserve Bank of Australia.
Granger, C.J. & D.F. Hendry (2005) A dialogue concerning a new instrument for econometric modeling. Econometric Theory (this issue).Google Scholar
Hansen, B.E. (2005) Challenges for econometric model selection. Econometric Theory (this issue).Google Scholar
Hendry, D.F. (1995) Dynamic Econometrics. Oxford University Press.
Hendry, D.F. & H.-M. Krolzig (1999) Improving on “Data mining reconsidered” by K.D. Hoover and S.J. Perez. Econometrics Journal 2, 41–58.Google Scholar
Hendry, D.F. & H.-M. Krolzig (2001) Automatic Econometric Model Selection. Timberlake Consultants Press.
Hendry, D.F. & H.-M. Krolzig (2002) New developments in automatic general-to-specific modelling. In B.P. Stigum (ed.), Econometrics and the Philosophy of Economics, pp. 379–419 (Chap. 16). MIT Press.
Hendry, D.F. & M.S. Morgan (1995) Introduction. The Foundations of Econometric Analysis. Cambridge University Press.
Hong, Y. (2001) Wavelet-Based Estimation for Heteroskedastic and Autocorrelation Consistent Variance-Covariance Matrices. Working paper, Cornell University.
Hoover, K.D. (2005) Automatic inference of the contemporaneous causal order of a system of equations. Econometric Theory (this issue).Google Scholar
Hoover, K.D. & S.J. Perez (1999) Data mining reconsidered: Encompassing and the general-to-specific approach to specification search. Econometrics Journal 2, 167–191.Google Scholar
Jansson, M. (2004) On the error of rejection probability in simple autocorrelation robust tests. Econometrica 72, 937–946.Google Scholar
Judge, G.G. & M.E. Bock (1978) The Statistical Implications of Pre-Test and Stein-Rule Estimators in Econometrics. North-Holland.
Kabaila, P. (1995) The effect of model selection on confidence regions and prediction regions. Econometric Theory 11, 537–549.Google Scholar
Kaboudan, M.A. (2000) Genetic programming prediction of stock prices. Computational Economics 16, 207–236.Google Scholar
Keuzenkamp, H.A. (2000) Probability, Econometrics and Truth. Cambridge University Press.
Kiefer, N.M. & T.J. Vogelsang (2002a) Heteroskedasticity-autocorrelation robust testing using bandwidth equal to sample size. Econometric Theory 18, 1350–1366.Google Scholar
Kiefer, N.M. & T.J. Vogelsang (2002b) Heteroskedasticity-autocorrelation robust standard errors using the Bartlett kernel without truncation. Econometrica 70, 2093–2095.Google Scholar
Kiefer, N.M., T.J. Vogelsang, & H. Bunzel (2000) Simple robust testing of regression hypotheses. Econometrica, 68, 695–714.Google Scholar
Kim, C.-S. & P.C.B. Phillips (1999) Fully Modified Estimation of Fractional Cointegration Models. Working paper, Cowles Foundation, Yale University.
Kuersteiner, G.M. (2005) Automatic inference for infinite order vector autoregressions. Econometric Theory (this issue).Google Scholar
Leamer, E.E. (1978) Specification Searches: Ad hoc Inferences with Nonexperimental Data. Wiley.
Lee, J. & Y. Hong (2001) Testing for serial correlation of unknown form using wavelet methods. Econometric Theory 17, 386–423.Google Scholar
Lee, J.S. (1996) On the power of stationary tests using optimal bandwidth estimates. Economics Letters 51, 131–137.Google Scholar
Leeb, H. & B.M. Pötscher (2003a) The finite sample distribution of post-model-selection estimators and uniform versus nonuniform approximations. Econometric Theory 19, 100–142.Google Scholar
Leeb, H. & B.M. Pötscher (2003b) Can One Estimate the Conditional Distribution of Post Model Selection Estimators? Cowles Foundation Discussion paper 1444, Yale University.
Leeb, H. & B.M. Pötscher (2005) Model selection and inference: Facts and fiction. Econometric Theory (this issue).Google Scholar
Levitt, S. (2004) Interview: The really dismal scientist. Wired Magazine p. 56.
Linton, O. (2005) Nonparametric inference for unbalanced time series data. Econometric Theory (this issue).Google Scholar
Lucas, R.E. (1976) Econometric policy evaluation: A critique. In K. Brunner & A.H. Melzer (eds.), The Phillips Curve and Labor Markets. Journal of Monetary Economics, supplement, special issue, 1, 19–46.Google Scholar
Malinvaud, E. (1966) The statistical methods of econometrics. North-Holland.
Milev, J. (2004) Search for a Structural Specification of the Earnings-Returns Relation. Working paper, Yale University.
Paruolo, P. (2005) Automated inference and the future of econometrics: A comment. Econometric Theory (this issue).Google Scholar
Pearl, J. (2000) Causality: Models, Reasoning and Inference. Cambridge University Press.
Perez-Amaral, T., G.M. Gallo, & H. White (2005) A comparison of complementary automatic modeling methods: RETINA and PcGets. Econometric Theory (this issue).Google Scholar
Pesaran, M.H. & A. Timmermann (1995) The robustness and economic significance of predictability of stock returns. Journal of Finance 50, 1201–1228.Google Scholar
Pesaran, M.H. & A. Timmermann (2000) A recursive modeling approach to predicting UK stock returns. Economic Journal 110, 159–191.Google Scholar
Pesaran, M.H. & A. Timmermann (2005) Real time econometrics. Econometric Theory (this issue).Google Scholar
Phillips, P.C.B. (1991a) Optimal inference in cointegrated systems. Econometrica 59, 283–306.Google Scholar
Phillips, P.C.B. (1991b) Spectral regression for cointegrated time series. In W. Barnett, J. Powell, & G. Tauchen (eds.), Nonparametric and Semiparametric Methods in Economics and Statistics, pp. 413–435. Cambridge University Press.
Phillips, P.C.B. (1992) Bayes Methods for Trending Multiple Time Series with an Empirical Application to the US Economy. Cowles Foundation Discussion paper 1025.
Phillips, P.C.B. (1995a) Automated forecasts of Asia-Pacific economic activity. Asia-Pacific Economic Review 1, 92–102.Google Scholar
Phillips, P.C.B. (1995b) Bayesian model selection and prediction with empirical applications. Journal of Econometrics 69, 289–331.Google Scholar
Phillips, P.C.B. (1996) Econometric model determination. Econometrica 64, 763–812.Google Scholar
Phillips, P.C.B. (1998a) Impulse response and forecast error variance asymptotics in nonstationary VARs. Journal of Econometrics 83, 21–56.Google Scholar
Phillips, P.C.B. (1998b) Econometric analysis of nonstationary data. IMF Lectures, Cowles Foundation for Research in Economics.
Phillips, P.C.B. (2003) Laws and limits of econometrics. Economic Journal 113, C26–C52.Google Scholar
Phillips, P.C.B. (2005) HAC estimation by automated regression. Econometric Theory (this issue).Google Scholar
Phillips, P.C.B. & W. Ploberger (1994) Posterior odds testing for a unit root with data-based model selection. Econometric Theory 10, 774–808.Google Scholar
Phillips, P.C.B. & W. Ploberger (1996) An asymptotic theory of Bayesian inference for time series. Econometrica 64, 381–413.Google Scholar
Phillips, P.C.B. & K. Shimotsu (2004) Local Whittle estimation in nonstationary and unit root cases. Annals of Statistics 32, 656–692.Google Scholar
Phillips, P.C.B., Y. Sun, & S. Jin (2003a) Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation. Cowles Foundation Discussion paper 1407, Yale University.
Phillips, P.C.B., Y. Sun, & S. Jin (2003b) Long Run Variance Estimation Using Steep Origin Kernels without Truncation. Cowles Foundation Discussion paper 1437, Yale University.
Popper, K. (1959) The Logic of Scientific Discovery. Hutschinson.
Popper, K. (1963) Conjectures and Refutations. Routledge and Kegan Paul.
Pötscher, B.M. (1991) Effects of model selection on inference. Econometric Theory 7, 163–185.Google Scholar
Robinson, P.M. (1995) Gaussian semiparametric estimation of long range dependence. Annals of Statistics 23, 1630–1661.Google Scholar
Robinson, P.M. (2005) Robust covariance matrix estimation: HAC estimates with long memory/antipersistence correction. Econometric Theory (this issue).Google Scholar
Robinson, P.M. & J. Hualde (2003) Cointegration in fractional systems with unknown integration orders. Econometrica 71, 1727–1766.Google Scholar
Sala-i-Martin, X.X. (1997) I just ran two million regressions. American Economic Review 87, 178–183.Google Scholar
Schiff, A. & P.C.B. Phillips (2000) Forecasting New Zealand's real GDP. New Zealand Economic Papers 34, 159–182.Google Scholar
Stock, J.H. & M.W. Watson (1999) Forecasting inflation. Journal of Monetary Economics 44, 293–335.Google Scholar
Sul, D., P.C.B. Phillips, & C.-Y. Choi (2003) Prewhitening Bias in HAC Estimation. Cowles Foundation Discussion paper 1436, Yale University.
Swanson, N.R. & C.W.J. Granger (1997) Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions. Journal of the American Statistical Association 92, 357–367.Google Scholar
White, H. (2000) A reality check for data snooping. Econometrica 68, 1097–1127.Google Scholar
Zellner, A. (2004) Statistics, Econometrics and Forecasting. Cambridge University Press.
Zellner, A., H.A. Keuzenkamp, & M. McAleer (2001) Simplicity, Inference and Modelling: Keeping It Sophisticatedly Simple. Cambridge University Press.