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Data-Driven Model Evaluation: A Test for Revealed Performance

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  • Jeffrey S. Racine
  • Christopher F. Parmeter

Abstract

When comparing two competing approximate models using a particular loss function, the one having smallest `expected true error' for that loss function is expected to lie closest to the underlying data generating process (DGP) given this loss function and is therefore to be preferred. In this chapter we consider a data-driven method for testing whether or not two competing approximate models are equivalent in terms of their expected true error (i.e., their expected performance on unseen data drawn from the same DGP). The proposed test is quite flexible with regards to the types of models that can be compared (i.e., nested versus non-nested, parametric versus nonparametric) and is applicable in cross-sectional and time-series settings. Moreover, in time-series settings our method overcomes two of the drawbacks associated with dominant approaches, namely, their reliance on only one split of the data and the need to have a sufficiently large `hold-out' sample for these tests to possess adequate power.

Suggested Citation

  • Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
  • Handle: RePEc:mcm:deptwp:2012-13
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    File URL: http://socserv.mcmaster.ca/econ/rsrch/papers/archive/2012-13.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Asaftei, Gabriel & Parmeter, Christopher F., 2010. "Market power, EU integration and privatization: The case of Romania," Journal of Comparative Economics, Elsevier, vol. 38(3), pages 340-356, September.
    2. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 1-20, March.
    3. Steven F. Koch & Jeffrey S. Racine, 2016. "Healthcare facility choice and user fee abolition: regression discontinuity in a multinomial choice setting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 927-950, October.
    4. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, Elsevier.
    5. Bontemps, Christophe & Racine, Jeffrey S. & Simioni, Michel, 2009. "Nonparametric vs parametric binary choice models: An empirical investigation," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49286, Agricultural and Applied Economics Association.
    6. repec:spr:empeco:v:53:y:2017:i:4:d:10.1007_s00181-016-1173-6 is not listed on IDEAS
    7. Richard A. Ashley & Christopher F. Parmeter, 2013. "Sensitivity Analysis of Inference in GMM Estimation With Possibly-Flawed Moment Conditions," Working Papers e07-40, Virginia Polytechnic Institute and State University, Department of Economics.

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    Keywords

    approximate; misspecified; model selection; predictive accuracy; data mining;

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