Evaluating Automatic Model Selection
AbstractWe evaluate automatically selecting the relevant variables in an econometric model from a large candidate set.� General-to-specific selection is outlined for a constant model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors (N < T) where T is the sample size, then evaluated in simulation experiments for N = 1000.� Comparisons with Autometrics (Doornik, 2009) show similar properties, but not restricted to orthogonal cases.� Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing, and evaluate Autometrics'�capability in dynamic models by its cost of search versus costs of inference.
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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 474.
Date of creation: 01 Jan 2010
Date of revision:
Model selection; Autometrics; Post-selection bias correction; Costs of search; Costs of inference;
Other versions of this item:
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
- Jerzy Mycielski & Michal Kurcewicz, 2004. "A Specification Search Algorithm for Cointegrated Systems," Computing in Economics and Finance 2004 321, Society for Computational Economics.
- Jennifer Castle & David Hendry & Jurgen A. Doornik, 2008.
"Model Selection when there are Multiple Breaks,"
Economics Series Working Papers
407, University of Oxford, Department of Economics.
- 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.
- Carlos Santos & David Hendry & Soren Johansen, 2008.
"Automatic selection of indicators in a fully saturated regression,"
Springer, vol. 23(2), pages 317-335, April.
- David Hendry & Søren Johansen & Carlos Santos, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 337-339, April.
- Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2009. "How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms," Working Papers in Economics 09/13, University of Canterbury, Department of Economics and Finance.
- Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
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