On the harm that ignoring pretesting can cause

Journal of Econometrics

Volumn 122, Issue 1, 2004, Pages 27-46

  Danilov, Dmitry ^a   Magnus, Jan R ^b  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b TILBURG UNIVERSITY   (Netherlands)

Author keywords

Mean squared error; Model selection; Pretest estimator

Indexed keywords

ERROR ANALYSIS; MATHEMATICAL MODELS; MATRIX ALGEBRA; PARAMETER ESTIMATION; RISK ASSESSMENT;

MEAN SQUARED ERRORS; MODEL SELECTION; PRETEST ESTIMATORS;

DATA MINING;

EID: 3042730371     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2003.10.018     Document Type: Article

References (24)

1. Abadir K.M., Magnus J.R. Notation in econometrics. a proposal for a standard The Econometrics Journal. 5:2002;76-90.
2. Bancroft T.A. On biases in estimation due to the use of preliminary tests of significance. Annals of Mathematical Statistics. 15:1944;190-204.
3. Chatfield C. Model uncertainty, data mining and statistical inference. (with discussion) Journal of the Royal Statistical Society, Series A. 158:1995;419-466.
4. Giles J.A., Giles D.E.A. Pre-test estimation and testing in econometrics. recent developments Journal of Economic Surveys. 7:1993;145-197.
5. Giles J.A., Srivastava V. The exact distribution of a least-squares regression coefficient after a preliminary. t -test Statistics and Probability Letters. 16:1993;59-64.
6. Hendry D.F. Achievements and challenges in econometric methodology. Journal of Econometrics. 100:2001;7-10.
7. Hoover K.D., Perez S.J. Data mining reconsidered; encompassing and the general-to-specific approach to specification search. The Econometrics Journal. 2:1999;1-25.
8. Huntsberger D.V. A generalization of a preliminary testing procedure for pooling data. Annals of Mathematical Statistics. 26:1955;734-743.
9. Judge G.G., Bock M.E. The Statistical Implications of Pre-Test and Stein-rule Estimators in Econometrics. 1978;North-Holland, Amsterdam.
10. Judge, G.G., Bock, M.E., 1983. Biased estimation. In: Griliches, Z., Intriligator, M.D. (Eds.), Handbook of Econometrics, Vol. 1. North-Holland, Amsterdam (Chapter 10).
11. Leeb H., Pötscher B.M. The finite-sample distribution of post-model-selection estimators, and uniform versus non-uniform approximations. Econometric Theory. 19:2003;100-142.
12. Lovell M.C. Data mining. The Review of Economics and Statistics. 65:1983;1-12.
13. Magnus J.R. The traditional pretest estimator. Theory of Probability and its Applications. 44:1999;293-308.
14. Magnus J.R. Estimation of the mean of a univariate normal distribution with known variance. The Econometrics Journal. 5:2002;225-236.
15. Magnus J.R., Durbin J. Estimation of regression coefficients of interest when other regression coefficients are of no interest. Econometrica. 67:1999;639-643.
16. Mittelhammer R.C. Restricted least squares, pre-test, OLS and Stein-rule estimators: Risk comparisons under model misspecification. Journal of Econometrics. 25:1984;151-164.
17. Mosteller F. On pooling data. Journal of the American Statistical Association. 43:1948;231-242.
18. Pötscher B.M. Effects of model selection on inference. Econometric Theory. 7:1991;163-185.
19. Pötscher B.M., Novak A.J. The distribution of estimators after model selection. large and small sample results Journal of Statistical Computation and Simulation. 60:1998;19-56.
20. Roehrig C.S. Optimal critical regions for pre-test estimators using a Bayes risk criterion. Journal of Econometrics. 25:1984;3-14.
21. Sclove S.L., Morris C., Radhakrishnan R. Non-optimality of preliminary-test estimators for the mean of a multivariate normal distribution. The Annals of Mathematical Statistics. 43:1972;1481-1490.
22. Sen P.K. Asymptotic properties of maximum likelihood estimators based on conditional specification. Annals of Statistics. 7:1979;1019-1033.
23. Thomson M., Schmidt P. 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. 64:1982;174-176.
24. Zhang P. On the distributional properties of model selection criteria. Journal of the American Statistical Association. 87:1992;732-737.