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Model Selection in Equations with Many 'Small' Effects

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  • Jennifer Castle
  • David Hendry

Abstract

General unrestricted models (GUMs) may include important individual determinants, many small relevant effects, and irrelevant variables.� Automatic model selection procedures can handle perfect collinearity and more candidate variables than observations, allowing substantial dimension reduction from GUMs with salient regressors, lags, non-linear transformations, and multiple location shifts, together with all the principal components representing 'factor' structures, which can also capture small influences that selection may not retain individually.� High dimensional GUMs and even the final model can implicitly include more variables than observations entering via 'factors'.� We simulate selection in several special cases to illustrate.

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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 528.

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Date of creation: 01 Feb 2011
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Handle: RePEc:oxf:wpaper:528

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Keywords: Model selection; high dimensionality; principal components; non-linearity; Monte Carlo;

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  1. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-304, September.
  2. 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.
  3. Søren Johansen & Bent Nielsen, 2008. "An analysis of the indicator saturation estimator as a robust regression," Discussion Papers 08-03, University of Copenhagen. Department of Economics.
  4. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  5. David Hendry & Jennifer L. Castle, 2010. "Model Selection in Under-specified Equations Facing Breaks," Economics Series Working Papers 509, University of Oxford, Department of Economics.
  6. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
  7. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.
  8. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
  9. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," CEPR Discussion Papers 6706, C.E.P.R. Discussion Papers.
  10. Castle, Jennifer L. & Hendry, David F., 2010. "A low-dimension portmanteau test for non-linearity," Journal of Econometrics, Elsevier, vol. 158(2), pages 231-245, October.
  11. David Hendry & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Economics Papers 2003-W14, Economics Group, Nuffield College, University of Oxford.
  12. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
  13. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
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Cited by:
  1. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
  2. David Hendry & Felix Pretis, 2011. "Anthropogenic Influences on Atmospheric CO2," Economics Series Working Papers 584, University of Oxford, Department of Economics.
  3. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.

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