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Discussion of 'Data mining reconsidered'

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  • BRUCE E. HANSEN

Abstract

The Hoover?Perez description of the LSE [London School of Economics] general-to-specific methodology of model selection is formalized and analysed using the the-ory of model selection. Numerical evidence is provided to justify the claim that simple and elegant information criteria (which are easy to implement in applications) work at least as well, if not better, than the complicated algorithm attributed to the LSE methodology.

Suggested Citation

  • Bruce E. Hansen, 1999. "Discussion of 'Data mining reconsidered'," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 192-201.
    • Handle: RePEc:ect:emjrnl:v:2:y:1999:i:2:p:192-201
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    Citations

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    Cited by:

    1. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
    2. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
    3. Aris Spanos, 2018. "Mis†Specification Testing In Retrospect," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 541-577, April.
    4. David L. Weakliem, 2004. "Introduction to the Special Issue on Model Selection," Sociological Methods & Research, , vol. 33(2), pages 167-187, November.
    5. Peter Sandholt Jensen & Allan H. Würtz, 2006. "On determining the importance of a regressor with small and undersized samples," Economics Working Papers 2006-08, Department of Economics and Business Economics, Aarhus University.
    6. Peter-Jan Engelen, 2006. "Difficulties in the criminal prosecution of insider trading—A clinical study of the Bekaert case," European Journal of Law and Economics, Springer, vol. 22(2), pages 121-141, September.
    7. You, Wen & Davis, George C. & Nayga, Rodolfo M., Jr. & McIntosh, Alex, 2005. "Parental Time and Children's Obesity Measures," 2005 Annual meeting, July 24-27, Providence, RI 19386, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. John D. Hey & Gianna Lotito & Anna Maffioletti, 2018. "The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 8, pages 189-219, World Scientific Publishing Co. Pte. Ltd..
    9. Harris Dellas & Kevin Hoover, 2003. "Truth and Robustness in Cross-country Growth Regressions," Working Papers 11, University of California, Davis, Department of Economics.
    10. Godfrey, L.G. & Tremayne, A.R., 2005. "The wild bootstrap and heteroskedasticity-robust tests for serial correlation in dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 377-395, April.
    11. Bryant, Henry L. & Davis, George C., 2003. "Information Based Model Averaging And Internal Metanalysis In Seemingly Unrelated Regressions With An Application To A Demand System," 2003 Annual meeting, July 27-30, Montreal, Canada 21918, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Benner, Joachim & Meier, Carsten-Patrick, 2003. "Prognosegüte alternativer Frühindikatoren für die Konjunktur in Deutschland," Kiel Working Papers 1139, Kiel Institute for the World Economy (IfW Kiel).

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