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Model Selection in Equations with Many 'Small' Effects

  • Jennifer L. Castle

    ()

    (Magdalen College and Institute for New Economic Thinking, Oxford Martin School, University of Oxford, UK)

  • Jurgen A. Doornik

    ()

    (Economics Department and Institute for New Economic Thinking, Oxford Martin School, University of Oxford, UK)

  • David F. Hendry

    ()

    (Economics Department and Institute for New Economic Thinking, Oxford Martin School, University of Oxford, UK)

High dimensional general unrestricted models (GUMs) may include important individual determinants, many small relevant effects, and irrelevant variables. Automatic model selection procedures can handle 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, possibly representing 'factor' structures, as perfect collinearity is also unproblematic. 'Factors' can capture small influences that selection may not retain individually. 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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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 53_12.

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Date of creation: Jul 2012
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Handle: RePEc:rim:rimwps:53_12
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  1. David Hendry & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Economics Papers 2003-W14, Economics Group, Nuffield College, University of Oxford.
  2. 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.
  3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  4. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
  5. 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.
  6. Hannes Leeb & Benedikt M. Poetscher, 2000. "The Finite-Sample Distribution of Post-Model-Selection Estimators, and Uniform Versus Non-Uniform Approximations," Econometrics 0004001, EconWPA.
  7. 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.
  8. 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.
  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., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
  11. 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.
  12. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
  13. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.
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