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On the harm that pretesting does

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Author Info
Danilov, D.L.
Magnus, J.R. (Tilburg University, Center for Economic Research)

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Abstract

Data in econometrics are, as a rule, non-experimental and hence we have to use the same data set to select the model and also to estimate the parameters in the selected model. In standard applied econometrics practice, however, one reports zero bias and some variance of the (pretest) estimators conditional on the selected model. In this paper we find the unconditional moments of the pretest estimator, taking full account of the fact that model selection and estimation are an integrated procedure. We derive the bias, variance, and mean squared error of the pretest estimator, and show what the error is in not reporting the correct moments. This error can be very substantial. We also show that there can be large differences in underreporting between different model selection procedures. Finally, we ask how the underreporting error increases when the number of auxiliary regressors increases.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 37.

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Date of creation: 2001
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Handle: RePEc:dgr:kubcen:200137

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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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.:
  1. Judge, G.G. & Bock, M.E., 1983. "Biased estimation," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 10, pages 599-649 Elsevier. [Downloadable!] (restricted)
  2. Roehrig, C.S., 1984. "Optimal critical regions for pre-test estimators using a Bayes risk criterion," Journal of Econometrics, Elsevier, vol. 25(1-2), pages 3-14. [Downloadable!] (restricted)
  3. repec:cup:etheor:v:7:y:1991:i:2:p:163-85 is not listed on IDEAS
  4. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February. [Downloadable!] (restricted)
  5. Thomson, Michael & Schmidt, Peter, 1982. "A Note on the Comparison of the Mean Square Error of Inequality Constrained Least Squares and Other Related Estimators," The Review of Economics and Statistics, MIT Press, vol. 64(1), pages 174-76, February. [Downloadable!] (restricted)
  6. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 3(3), pages 431-67. [Downloadable!] (restricted)
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  7. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September. [Downloadable!] (restricted)
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  8. Jan R. Magnus & J. Durbin, 1999. "Estimation of Regression Coefficients of Interest When Other Regression Coefficients Are of No Interest," Econometrica, Econometric Society, vol. 67(3), pages 639-644, May.
  9. Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics. [Downloadable!]
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  10. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  11. Giles, Judith A & Giles, David E A, 1993. " Pre-test Estimation and Testing in Econometrics: Recent Developments," Journal of Economic Surveys, Blackwell Publishing, vol. 7(2), pages 145-97, June.
  12. Feldstein, Martin S, 1973. "Multicollinearity and the Mean Square Error of Alternative Estimators," Econometrica, Econometric Society, vol. 41(2), pages 337-46, March. [Downloadable!] (restricted)
  13. Hannes Leeb & Benedikt M. Poetscher, 2000. "The Finite-Sample Distribution of Post-Model-Selection Estimators, and Uniform Versus Non-Uniform Approximations," Econometrics 0004001, EconWPA. [Downloadable!]
  14. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  15. P?tscher, B.M., 1991. "Effects of Model Selection on Inference," Econometric Theory, Cambridge University Press, vol. 7(02), pages 163-185, June. [Downloadable!]
  16. Hendry, David F., 2001. "Achievements and challenges in econometric methodology," Journal of Econometrics, Elsevier, vol. 100(1), pages 7-10, January. [Downloadable!] (restricted)
  17. Zaman, A., 1984. "Avoiding model selection by the use of shrinkage techniques," Journal of Econometrics, Elsevier, vol. 25(1-2), pages 73-85. [Downloadable!] (restricted)
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(explanations, 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.)

  1. Chua, C.L. & Griffiths, W.E. & O'Donnell, C.J., 2001. "Bayesian Model Averaging in Consumer Demand Systems with Inequality Constraints," Department of Economics - Working Papers Series 806, The University of Melbourne. [Downloadable!]
  2. Danilov, D. & Magnus, J.R., 2002. "Forecast accuracy after pretesting with an application to the stock market," Discussion Paper 76, Tilburg University, Center for Economic Research. [Downloadable!]
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  3. Chotikapanich, D. & Griffiths, W.E. & Rao, D.S.P., 2001. "Averaging Income Distributions," Department of Economics - Working Papers Series 798, The University of Melbourne. [Downloadable!]
    Other versions:
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