Using Model Selection Algorithms To Obtain Reliable Coefficient Estimates
This review surveys a number of common Model Selection Algorithms (MSAs), discusses how they relate to each other, and identifies factors that explain their relative performances. At the heart of MSA performance is the trade-off between Type I and Type II errors. Some relevant variables will be mistakenly excluded, and some irrelevant variables will be retained by chance. A successful MSA will find the optimal trade-off between the two types of errors for a given data environment. Whether a given MSA will be successful in a given environment depends on the relative costs of these two types of errors. We use Monte Carlo experimentation to illustrate these issues. We confirm that no MSA does best in all circumstances. Even the worst MSA in terms of overall performance – the strategy of including all candidate variables – sometimes performs best (viz., when all candidate variables are relevant). We also show how (i) the ratio of relevant to total candidate variables and (ii) DGP noise affect relative MSA performance. Finally, we discuss a number of issues complicating the task of MSAs in producing reliable coefficient estimates.
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Volume (Year): 27 (2013)
Issue (Month): 2 (04)
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- McAleer, Michael & Pagan, Adrian R & Volker, Paul A, 1985.
"What Will Take the Con out of Econometrics?,"
American Economic Review,
American Economic Association, vol. 75(3), pages 293-307, June.
- Peter C.B. Phillips, 2004.
"Automated Discovery in Econometrics,"
Cowles Foundation Discussion Papers
1469, Cowles Foundation for Research in Economics, Yale University.
- Sune Karlsson & Tor Jacobson, 2004.
"Finding good predictors for inflation: a Bayesian model averaging approach,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
- Jacobson, Tor & Karlsson, Sune, 2002. "Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach," Working Paper Series 138, Sveriges Riksbank (Central Bank of Sweden).
- Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011.
"Evaluating Automatic Model Selection,"
Journal of Time Series Econometrics,
De Gruyter, vol. 3(1), pages 1-33, February.
- Leeb, Hannes & P tscher, Benedikt M., 2003.
"The Finite-Sample Distribution Of Post-Model-Selection Estimators And Uniform Versus Nonuniform Approximations,"
Cambridge University Press, vol. 19(01), pages 100-142, February.
- Hannes Leeb & Benedikt M. Poetscher, 2000. "The Finite-Sample Distribution of Post-Model-Selection Estimators, and Uniform Versus Non-Uniform Approximations," Econometrics 0004001, EconWPA.
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