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MODEL DISCOVERY AND TRYGVE HAAVELMO’S LEGACY

Published online by Cambridge University Press:  20 June 2014

David F. Hendry  and
Søren Johansen
David F. Hendry*
Affiliation:
University of Oxford
Søren Johansen
Affiliation:
University of Copenhagen and Aarhus University
*
*Address correspondence to David F. Hendry, Institute for New Economic Thinking, University of Oxford, Eagle House, Walton Well Road, Oxford OX2 6ED, UK. email: [email protected]
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Abstract

Trygve Haavelmo’s Probability Approach aimed to implement economic theories, but he later recognized their incompleteness. Although he did not explicitly consider model selection, we apply it when theory-relevant variables, {Xt}, are retained without selection while selecting other candidate variables, {Wt}. Under the null that the {Wt} are irrelevant, by orthogonalizing with respect to the {Xt}, the estimator distributions of the Xt’s parameters are unaffected by selection even for more variables than observations and for endogenous variables. Under the alternative, when the joint model nests the generating process, an improved outcome results from selection. This implements Haavelmo’s program relatively costlessly.

Type
ARTICLES
Information
Econometric Theory , Volume 31 , Issue 1: Haavelmo Memorial Issue: Part One , February 2015 , pp. 93 - 114
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Aldrich, J. (1989) Autonomy. Oxford Economic Papers 41, 15–34.CrossRefGoogle Scholar
Aldrich, J. (1994) Haavelmo’s identification theory. Econometric Theory 10, 198–219.CrossRefGoogle Scholar
Anderson, T.W. (1962) The choice of the degree of a polynomial regression as a multiple-decision problem. The Annals of Mathematical Statistics 33, 255–265.CrossRefGoogle Scholar
Bjerkholt, O. (2005) Frisch’s econometric laboratory and the rise of Trygve Haavelmo’s probability approach. Econometric Theory 21, 491–533.CrossRefGoogle Scholar
Bjerkholt, O. (2007) Writing the probability approach with nowhere to go: Haavelmo in the United States, 1939–1944. Econometric Theory 23, 775–837.CrossRefGoogle Scholar
Bock, M.E., Yancey, T.A., & Judge, G.C. (1973) Statistical consequences of preliminary test estimators in regression. Journal of the American Statistical Association 68, 109–116.CrossRefGoogle Scholar
Castle, J.L., Doornik, J.A., & Hendry, D.F. (2011) Evaluating automatic model selection. Journal of Time Series Econometrics 3(1); doi:10.2202/1941–1928.1097.CrossRefGoogle Scholar
Castle, J.L., Doornik, J.A., & Hendry, D.F. (2012) Model selection when there are multiple breaks. Journal of Econometrics 169, 239–246.CrossRefGoogle Scholar
Castle, J.L., Fawcett, N.W.P., & Hendry, D.F. (2010) Forecasting with equilibrium-correction models during structural breaks. Journal of Econometrics 158, 25–36.CrossRefGoogle Scholar
Castle, J.L. & Shephard, N. (eds.) (2009) The Methodology and Practice of Econometrics. Oxford University Press.CrossRefGoogle Scholar
Chatfield, C. (1995) Model uncertainty, data mining and statistical inference (with discussion).Journal of the Royal Statistical Society, A 158, 419–466.CrossRefGoogle Scholar
Clements, M.P. & Hendry, D.F. (1998) Forecasting Economic Time Series. Cambridge University Press.CrossRefGoogle Scholar
Clements, M.P. & Hendry, D.F. (eds.) (2002) A Companion to Economic Forecasting. Blackwell.Google Scholar
Clements, M.P. & Hendry, D.F. (eds.) (2011) Oxford Handbook of Economic Forecasting. Oxford University Press.CrossRefGoogle Scholar
Coen, P.G., Gomme, E.D., & Kendall, M.G. (1969) Lagged relationships in economic forecasting. Journal of the Royal Statistical Society A 132, 133–163.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (2004) Econometric Theory and Methods. Oxford University Press.Google Scholar
Dickey, D.A. & 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
Doob, J.L. (1953) Stochastic Processes, 1990 ed. John Wiley Classics Library.Google Scholar
Doornik, J.A. (2007) Econometric Model Selection with More Variables than Observations. Working paper, Economics Department, University of Oxford.Google Scholar
Doornik, J.A. (2008) Encompassing and automatic model selection. Oxford Bulletin of Economics and Statistics 70, 915–925.CrossRefGoogle Scholar
Doornik, J.A. (2009) Autometrics. See Castle and Shephard (2009), pp. 88–121.Google Scholar
Doornik, J.A. & Hendry, D.F. (2009) Empirical Econometric Modelling using PcGive: Volume I. Timberlake Consultants Press.Google Scholar
Durbin, J. (1954) Errors in variables. Review of the Institute of International Statistics 22, 23–54.CrossRefGoogle Scholar
Eddington, C. (1928) Space, Time, and Gravitation. Cambridge University Press.Google Scholar
Elliott, G., Granger, C.W.J., & Timmermann, A. (eds.) (2006) Handbook of Econometrics on Forecasting. Elsevier.Google Scholar
Engle, R.F. & Granger, C.W.J. (1987) Cointegration and error correction: Representation, estimation and testing. Econometrica 55, 251–276.CrossRefGoogle Scholar
Engle, R.F. & Hendry, D.F. (1993) Testing super exogeneity and invariance in regression models. Journal of Econometrics 56, 119–139.CrossRefGoogle Scholar
Engle, R.F., Hendry, D.F., & Richard, J.F. (1983) Exogeneity. Econometrica 51, 277–304.CrossRefGoogle Scholar
Frisch, R. (1938) Statistical versus theoretical relations in economic macrodynamics. Mimeograph dated 17 July 1938, League of Nations Memorandum. Reproduced by University of Oslo in 1948 with Tinbergen’s comments. Contained in Memorandum ‘Autonomy of Economic Relations’, 6 November 1948, Oslo, Universitets Økonomiske Institutt. Reprinted in D.F. Hendry & M.S. Morgan (1995) The Foundations of Econometric Analysis. Cambridge University Press.Google Scholar
Frisch, R. & Waugh, F.V. (1933) Partial time regression as compared with individual trends. Econometrica 1, 221–223.CrossRefGoogle Scholar
Granger, C.W.J. (1981) Some properties of time series data and their use in econometric model specification. Journal of Econometrics 16, 121–130.CrossRefGoogle Scholar
Haavelmo, T. (1944) The probability approach in econometrics. Econometrica 12, 1–118, supplement.CrossRefGoogle Scholar
Haavelmo, T. (1958) The role of the econometrician in the advancement of economic theory. Econometrica 26, 351–357.CrossRefGoogle Scholar
Haavelmo, T. (1989) Prize Lecture. Sveriges Riksbank: Prize in Economic Sciences in Memory of Alfred Nobel.Google Scholar
Hausman, J.A. (1978) Specification tests in econometrics. Econometrica 46, 1251–1271.CrossRefGoogle Scholar
Hendry, D.F. (2000) Econometrics: Alchemy or Science?, New ed. Oxford University Press.CrossRefGoogle Scholar
Hendry, D.F. (2006) Robustifying forecasts from equilibrium-correction models. Journal of Econometrics 135, 399–426.CrossRefGoogle Scholar
Hendry, D.F. (2009) The methodology of empirical econometric modeling: Applied econometrics through the looking-glass. In Mills, T.C. & Patterson, K.D. (eds.), Palgrave Handbook of Econometrics, pp. 3–67. Palgrave MacMillan.CrossRefGoogle Scholar
Hendry, D.F. (2011a) Empirical economic model discovery and theory evaluation. Rationality, Markets and Morals 2, 115–145. Available at http://www.rmm-journal.de/htdocs/st01.html.Google Scholar
Hendry, D.F. (2011b) On adding over-identifying instrumental variables to simultaneous equations. Economics Letters 111, 68–70.CrossRefGoogle Scholar
Hendry, D.F., Johansen, S., & Santos, C. (2008) Automatic selection of indicators in a fully saturated regression. Computational Statistics 33, 317–335. Erratum, 337–339.Google Scholar
Hendry, D.F. & Krolzig, H.M. (2005) The properties of automatic Gets modelling. Economic Journal 115, C32–C61.CrossRefGoogle Scholar
Hendry, D.F. & Morgan, M.S. (eds.) (1995) The Foundations of Econometric Analysis. Cambridge University Press.CrossRefGoogle Scholar
Hendry, D.F. & Santos, C. (2010) An automatic test of super exogeneity. In Watson, M.W., Bollerslev, T., & Russell, J. (eds.), Volatility and Time Series Econometrics, pp. 164–193. Oxford University Press.Google Scholar
Hendry, D.F., Spanos, A., & Ericsson, N.R. (1989) The contributions to econometrics in Trygve Haavelmo’s The probability approach in econometrics. Sosialøkonomen 11, 12–17.Google Scholar
Hood, W.C. & Koopmans, T.C. (eds.) (1953) Studies in Econometric Method. Number 14 in Cowles Commission Monograph. John Wiley & Sons.Google Scholar
Hooker, R.H. (1901) Correlation of the marriage rate with trade. Journal of the Royal Statistical Society 64, 485–492.Google Scholar
Hoover, K.D. & Perez, S.J. (1999) Data mining reconsidered: Encompassing and the general-to-specific approach to specification search. Econometrics Journal 2, 167–191.CrossRefGoogle Scholar
Hoover, K.D. & Perez, S.J. (2000) Three attitudes towards data mining. Journal of Economic Methodology 7, 195–210.CrossRefGoogle Scholar
Johansen, S. (1988) Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12, 231–254.CrossRefGoogle Scholar
Johansen, S. (1995) Likelihood-based Inference in Cointegrated Vector Autoregressive Models.Oxford University Press.CrossRefGoogle Scholar
Johansen, S. & Nielsen, B. (2009) An analysis of the indicator saturation estimator as a robust regression estimator. See Castle and Shephard (2009), pp. 1–36.Google Scholar
Juselius, K. (1993) VAR modelling and Haavelmo’s probability approach to econometrics. Empirical Economics 18, 595–622.CrossRefGoogle Scholar
Keynes, J.M. (1939) Professor Tinbergen’s method. The Economic Journal 44, 558–568.CrossRefGoogle Scholar
Keynes, J.M. (1940) Statistical business-cycle research: Comment. The Economic Journal 50,154–156.CrossRefGoogle Scholar
Koopmans, T.C. (1947) Measurement without theory. Review of Economics and Statistics 29,161–179.CrossRefGoogle Scholar
Koopmans, T.C. (ed.) (1950) Statistical Inference in Dynamic Economic Models. Number 10 in Cowles Commission Monograph. John Wiley & Sons.Google Scholar
Krüger, L., Gigerenzer, G., & Morgan, M.S. (eds.) (1987) The Probabilistic Revolution, vol. 2. MIT Press.Google Scholar
Kurcewicz, M. & Mycielski, J. (2003) A Specification Search Algorithm for Cointegrated Systems. Discussion paper, Statistics Department, Warsaw University.Google Scholar
Leamer, E.E. (1978) Specification Searches. Ad-Hoc Inference with Non-Experimental Data.John Wiley.Google Scholar
Leeb, H. & Pötscher, B.M. (2003) The finite-sample distribution of post-model-selection estimators, and uniform versus non-uniform approximations. Econometric Theory 19, 100–142.CrossRefGoogle Scholar
Leeb, H. & Pötscher, B.M. (2005) Model selection and inference: Facts and fiction. Econometric Theory 21, 21–59.CrossRefGoogle Scholar
Liao, Z. & Phillips, P.C.B. (2012) Automated Estimation of Vector Error Correction Models. Discussion paper, Economics Department, Yale University.CrossRefGoogle Scholar
Lovell, M.C. (1983) Data mining. Review of Economics and Statistics 65, 1–12.Google Scholar
Lucas, R.E. (1976) Econometric policy evaluation: A critique. In Brunner, K. & Meltzer, A. (eds.), The Phillips Curve and Labor Markets. Carnegie-Rochester Conferences on Public Policy, vol. 1, pp. 19–46. North-Holland Publishing Company.Google Scholar
Marschak, J. & Lange, O. (1940) Mr. Keynes on the statistical verification of business cycle theories. See Hendry and Morgan (1995).CrossRefGoogle Scholar
Miller, P.J. (1978) Forecasting with econometric methods: A comment. Journal of Business 51, 579–586.CrossRefGoogle Scholar
Mills, T.C. (2010) Bradford Smith: An econometrician decades ahead of his time. Oxford Bulletin of Economics and Statistics 73, 276–285.CrossRefGoogle Scholar
Moene, K.A. & Rødseth, A. (1991) Nobel laureate: Trygve Haavelmo. Journal of Economic Perspectives 5, 175–192.CrossRefGoogle Scholar
Morgan, M.S. (1990) The History of Econometric Ideas. Cambridge University Press.CrossRefGoogle Scholar
Omtzig, P. (2002) Automatic identification and restriction of the cointegration space. Thesis chapter, Economics Department, Copenhagen University.Google Scholar
Pagan, A.R. (1987) Three econometric methodologies: A critical appraisal. Journal of Economic Surveys 1, 3–24.CrossRefGoogle Scholar
Phillips, P.C.B. (1986) Understanding spurious regressions in econometrics. Journal of Econometrics 33, 311–340.CrossRefGoogle Scholar
Phillips, P.C.B. (1991) Optimal inference in cointegrated systems. Econometrica 59, 283–306.CrossRefGoogle Scholar
Popper, K.R. (1959) The Logic of Scientific Discovery. Basic Books.Google Scholar
Popper, K.R. (1963) Conjectures and Refutations. Basic Books.Google Scholar
Rapach, D.E. & Wohar, M.E. (eds.) (2008) Forecasting in the Presence of Structural Breaks and Model Uncertainty. Emerald Group.CrossRefGoogle Scholar
Sargan, J.D. (2001) Model building and data mining. Econometric Reviews 20, 159–170. First presented to the Association of University Teachers of Economics, Manchester, 1973.CrossRefGoogle Scholar
Sims, C.A., Stock, J.H., & Watson, M.W. (1990) Inference in linear time series models with some unit roots. Econometrica 58, 113–144.CrossRefGoogle Scholar
Smith, B.B. (1926) Combining the advantages of first-difference and deviation-from-trend methods of correlating time series. Journal of the American Statistical Association 21, 55–59.Google Scholar
Smith, B.B. (1927) Forecasting the volume and value of the cotton crop. Journal of the American Statistical Association 22, 442–459.CrossRefGoogle Scholar
Smith, B.B. (1929) Judging the forecast for 1929. Journal of the American Statistical Association 24, 94–98.Google Scholar
Spanos, A. (1989) On re-reading Haavelmo: A retrospective view of econometric modeling. Econometric Theory 5, 405–429.CrossRefGoogle Scholar
Tinbergen, J. (1939) Statistical Testing of Business-Cycle Theories, vol. I, A Method and Its Application to Investment Activity. League of Nations.Google Scholar
Toda, H.Y. & Phillips, P.C.B. (1993) Vector autoregressions and causality. Econometrica 61, 1367–1393.CrossRefGoogle Scholar
Vining, R. (1949) A rejoinder. Review of Economics and Statistics 31, 91–94.CrossRefGoogle Scholar
Wu, D. (1973) Alternative tests of independence between stochastic regressors and disturbances. Econometrica 41, 733–750.CrossRefGoogle Scholar
Yule, G.U. (1926) Why do we sometimes get nonsense-correlations between time-series? A study in sampling and the nature of time series (with discussion). Journal of the Royal Statistical Society 89, 1–64.CrossRefGoogle Scholar