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Avoiding biases from data-dependent specification search: an application to a tillage choice model

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  • Sengupta, Sanchita
  • Kurkalova, Lyubov A.
  • Kling, Catherine L.

Abstract

The study evaluates the gains of avoiding data-dependent specification search on an estimation sample in an application to discrete choice models. We incorporate data splitting, the process by which the total available sample is randomly split in two or more sub-samples with the first (specification) sub-sample used for specification search, and the second (estimation) sub-sample used for obtaining clean estimates using the model chosen on the specification sub-sample according to a set criterion. We estimate 14 binary Logit models of the adoption of conservation tillage corresponding to the major sub-watersheds of the Upper Mississippi River Basin. For each of the sub-watershed models, we use the specification sub-sample to choose the explanatory variables that lead to the highest number of correct predictions provided that estimated coefficients are in conformity with economic theory. To evaluate the gains of avoiding specification search on the estimation sub-sample, we follow Gong (1986)[8] and calculate the expected excess error, which is a measure of excess optimism concerning model fit on the specification sample. We find that the excess optimism varies with the sub-watersheds and has a tendency to be larger for the sub-watersheds with smaller samples.

Suggested Citation

  • Sengupta, Sanchita & Kurkalova, Lyubov A. & Kling, Catherine L., 2006. "Avoiding biases from data-dependent specification search: an application to a tillage choice model," 2006 Annual meeting, July 23-26, Long Beach, CA 21399, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea06:21399
    DOI: 10.22004/ag.econ.21399
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    References listed on IDEAS

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    1. Veall, Michael R, 1992. "Bootstrapping the Process of Model Selection: An Econometric Example," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(1), pages 93-99, Jan.-Marc.
    2. Bailey Norwood & Matthew C. Roberts & Jayson L. Lusk, 2004. "Ranking Crop Yield Models Using Out-of-Sample Likelihood Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1032-1043.
    3. Lyubov Kurkalova & Catherine Kling & Jinhua Zhao, 2006. "Green Subsidies in Agriculture: Estimating the Adoption Costs of Conservation Tillage from Observed Behavior," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 54(2), pages 247-267, June.
    4. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    5. Pötscher, B.M., 1991. "Effects of Model Selection on Inference," Econometric Theory, Cambridge University Press, vol. 7(2), pages 163-185, June.
    6. Veall, Michael R & Zimmermann, Klaus F, 1996. "Pseudo-R-[superscript 2] Measures for Some Common Limited Dependent Variable Models," Journal of Economic Surveys, Wiley Blackwell, vol. 10(3), pages 241-259, September.
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