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CHALLENGES FOR ECONOMETRIC MODEL SELECTION

Published online by Cambridge University Press:  08 February 2005

Bruce E. Hansen
Bruce E. Hansen
Affiliation:
University of Wisconsin
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Abstract

Standard econometric model selection methods are based on four conceptual errors: parametric vision, the assumption of a true data generating process, evaluation based on fit, and ignoring the impact of model uncertainty on inference. Instead, econometric model selection methods should be based on a semiparametric vision, models should be viewed as approximations, models should be evaluated based on their purpose, and model uncertainty should be incorporated into inference methods. These problems have been examined individually but not jointly, and my view is that future research into econometric model selection should attempt to address all four issues.This research was supported by the National Science Foundation. I thank Peter Phillips and a referee for helpful comments that greatly improved the arguments and exposition.

Type
Research Article
Information
Econometric Theory , Volume 21 , Issue 1 , February 2005 , pp. 60 - 68
Copyright
© 2005 Cambridge University Press

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References

REFERENCES

Andrews, D.W.K. & B. Lu (2001) Consistent model and moment selection criteria for GMM estimation with applications to dynamic panel models. Journal of Econometrics 101, 123–164.Google Scholar
Berk, K.N. (1974) Consistent autoregressive spectral estimates. Annals of Statistics 2, 489–502.Google Scholar
Claeskens, G. & N.L. Hjort (2003) The focused information criterion. Journal of the American Statistical Association 98, 900–916.Google Scholar
Granger, C. & D. Hendry (2005) A dialogue concerning a new instrument for econometric modeling. Econometric Theory (this issue).Google Scholar
Hannan, E.J. & M. Deistler (1988) The Statistical Theory of Linear Systems. Wiley.
Hjort, N.L. & G. Claeskens (2003) Frequentist model average estimators. Journal of the American Statistical Association 98, 879–899.Google Scholar
Hoeting, J.A., D. Madigan, A.E. Raftery, & C.T. Volinsky (1999) Bayesian model averaging: A tutorial. Statistical Science 14, 382–417.Google Scholar
Hong, H., B. Preston, & M. Shum (2003) Generalized empirical likelihood-based model selection criteria for moment condition models. Econometric Theory 19, 923–943.Google Scholar
Kuersteiner, G.M. (2002) Efficient instrumental variables estimation for autoregressive models with conditional heteroskedasticity. Econometric Theory 18, 547–583.Google Scholar
Leeb, H. & B.M. Pötscher (2003a) Can One Estimate the Unconditional Distribution of Post-Model-Selection Estimators? Working paper, Department of Statistics, University of Vienna.
Leeb, H. & B.M. Pötscher (2003b) Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators? Working paper, Department of Statistics, University of Vienna.
Lewis, R. & G. Reinsel (1985) Prediction of multivariate time series by autoregressive model fitting. Journal of Multivariate Analysis 16, 393–411.Google Scholar
Phillips, P.C.B. (1995) Econometric model determination. Econometrica 64, 763–812.Google Scholar
Pötscher, B.M. (1991) The effect of model selection on inference. Econometric Theory 7, 163–185.Google Scholar