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On the Use of Holdout Samples for Model Selection

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  • Frank Schorfheide
  • Kenneth I. Wolpin

Abstract

Researchers often hold out data from the estimation of econometric models to use for external validation. However, the use of holdout samples is suboptimal from a Bayesian perspective, which prescribes using the entire sample to form posterior model weights. This paper examines a possible rationale for the use of holdout samples: data-inspired modifications of structural models are likely to lead to an exaggeration of model fit. The use of holdout samples can, in principle, set an incentive for the modeler not to exaggerate model fit.

Suggested Citation

  • Frank Schorfheide & Kenneth I. Wolpin, 2012. "On the Use of Holdout Samples for Model Selection," American Economic Review, American Economic Association, vol. 102(3), pages 477-481, May.
    • Handle: RePEc:aea:aecrev:v:102:y:2012:i:3:p:477-81
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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/aer.102.3.477
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    References listed on IDEAS

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    1. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    2. John Geweke, 2010. "Complete and Incomplete Econometric Models," Economics Books, Princeton University Press, edition 1, number 9218.
    3. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    4. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    5. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
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    Cited by:

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    2. Emrah Arbak, 2017. "Identifying the provisioning policies of Belgian banks," Working Paper Research 326, National Bank of Belgium.
    3. Schorfheide, Frank & Wolpin, Kenneth I., 2016. "To hold out or not to hold out," Research in Economics, Elsevier, vol. 70(2), pages 332-345.
    4. Banghua Zhu & Sai Praneeth Karimireddy & Jiantao Jiao & Michael I. Jordan, 2023. "Online Learning in a Creator Economy," Papers 2305.11381, arXiv.org.
    5. Banghua Zhu & Stephen Bates & Zhuoran Yang & Yixin Wang & Jiantao Jiao & Michael I. Jordan, 2022. "The Sample Complexity of Online Contract Design," Papers 2211.05732, arXiv.org, revised May 2023.
    6. Chen, Xiaomeng Charlene & Jones, Stewart & Hasan, Mostafa Monzur & Zhao, Ruoyun & Alam, Nurul, 2023. "Does strategic deviation influence firms’ use of supplier finance?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    7. Joel da Costa & Tim Gebbie, 2020. "Learning low-frequency temporal patterns for quantitative trading," Papers 2008.09481, arXiv.org.
    8. Kanczuk, Fabio, 2015. "Brazil Through the Eyes of CHORINHO," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(2), March.
    9. de Bresser, Jochem, 2021. "Evaluating the Accuracy of Counterfactuals The Role of Heterogeneous Expectations in Life Cycle Models," Discussion Paper 2021-034, Tilburg University, Center for Economic Research.
    10. Emrah Arbak, 2017. "Identifying the provisioning policies of Belgian banks," Working Paper Research 326, National Bank of Belgium.
    11. Maibom, Jonas, 2021. "The Danish Labor Market Experiments: Methods and Findings," Nationaløkonomisk tidsskrift, Nationaløkonomisk Forening, vol. 2021(1), pages 1-21.
    12. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    13. Cepeda Carrión, Gabriel & Henseler, Jörg & Ringle, Christian M. & Roldán, José Luis, 2016. "Prediction-oriented modeling in business research by means of PLS path modeling: Introduction to a JBR special section," Journal of Business Research, Elsevier, vol. 69(10), pages 4545-4551.
    14. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    15. de Bresser, Jochem, 2021. "Evaluating the Accuracy of Counterfactuals The Role of Heterogeneous Expectations in Life Cycle Models," Other publications TiSEM a7e2b4d8-fed0-4e86-926f-d, Tilburg University, School of Economics and Management.

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