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Evaluating Automatic Model Selection

Listed author(s):
  • Castle Jennifer L.

    (University of Oxford)

  • Doornik Jurgen A

    (University of Oxford)

  • Hendry David F.

    (University of Oxford)

We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors. Comparisons with an automated model selection algorithm, 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 as well as evaluate selection in dynamic models by costs of search versus costs of inference.

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Article provided by De Gruyter in its journal Journal of Time Series Econometrics.

Volume (Year): 3 (2011)
Issue (Month): 1 (February)
Pages: 1-33

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Handle: RePEc:bpj:jtsmet:v:3:y:2011:i:1:n:8
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  1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
  2. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  3. Hans-Martin Krolzig, 2003. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 769-801, December.
  4. Teodosio Perez-Amaral & Giampiero M. Gallo & Halbert White, 2003. "A Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 821-838, December.
  5. Judge, George G & Bock, M E, 1976. "A Comparison of Traditional and Stein-Rule Estimators under Weighted Squared Error Loss," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 17(1), pages 234-240, February.
  6. Phillips, Peter C B, 1996. "Econometric Model Determination," Econometrica, Econometric Society, vol. 64(4), pages 763-812, July.
  7. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
  8. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
  9. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, 03.
  10. Phillips, P C B, 1988. "Reflections on Econometric Methodology," The Economic Record, The Economic Society of Australia, vol. 64(187), pages 344-359, December.
  11. Kevin D. Hoover & Stephen J. Perez, 2004. "Truth and Robustness in Cross-country Growth Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 765-798, December.
  12. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
  13. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, September.
  14. 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.
  15. Hylleberg, Svend, 1986. "Seasonality in Regression," Elsevier Monographs, Elsevier, edition 1, number 9780123634559 edited by Shell, Karl.
  16. Peter C.B. Phillips, 1995. "Automated Forecasts of Asia-Pacific Economic Activity," Cowles Foundation Discussion Papers 1103, Cowles Foundation for Research in Economics, Yale University.
  17. 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.
  18. Leeb, Hannes & P tscher, Benedikt M., 2003. "The Finite-Sample Distribution Of Post-Model-Selection Estimators And Uniform Versus Nonuniform Approximations," Econometric Theory, Cambridge University Press, vol. 19(01), pages 100-142, February.
  19. Hans-Martin Krolzig, 2003. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 769-801, December.
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