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Exploring the Usefulness of a Non-Random Holdout Sample for Model Validation: Welfare Effects on Female Behavior

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  • Michael P. Keane

    ()
    (Department of Economics, Yale University)

  • Kenneth I. Wolpin

    ()
    (Department of Economics, University of Pennsylvania)

Abstract

Opportunities for external validation of behavioral models in the social sciences that are based on randomized social experiments or on large regime shifts, that can be treated as experiments for the purpose of model validation, are extremely rare. In this paper, we consider an alternative approach, namely mimicking the essential element of regime change by non-randomly holding out from estimation a portion of the sample that faces a significantly different policy regime. The non-random holdout sample is used for model validation/selection. We illustrate the non-random holdout sample approach to model validation in the context of a model of welfare program participation. The policy heterogeneity that we exploit to generate a non-random holdout sample takes advantage of the wide variation across states that has existed in welfare policy.

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Bibliographic Info

Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 06-006.

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Length: 85 pages
Date of creation: 01 May 2006
Date of revision:
Handle: RePEc:pen:papers:06-006

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Keywords: Model validation; Hold-out sample; Public welfare;

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  1. M. Keane & R. Mofitt, 1995. "A Structural Model of Multiple Welfare Program Participation and Labor Supply," Working Papers 95-4, Brown University, Department of Economics.
  2. Sauer, Robert & Keane, Michael P., 2007. "A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables," Discussion Paper Series In Economics And Econometrics 0705, Economics Division, School of Social Sciences, University of Southampton.
  3. Michael P. Keane & Kenneth I. Wolpin, 1994. "The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence," Staff Report 181, Federal Reserve Bank of Minneapolis.
  4. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
  5. Christian Bontemps & Jean-Marc Robin & Gérard J. Van Den Berg, 2000. "Equilibrium Search with Continuous Productivity Dispersion: Theory and Nonparametric Estimation," Working Papers 249986, Institut National de la Recherche Agronomique, France.
  6. Bontemps, C. & Robin, J.M. & van den Berg, G.J., 1998. "Equilibrium Search with Continuous Productivity Dispersion: Theory and Non-Parametric Estimation," Papers 98-07, Centre for Labour Market and Social Research, Danmark-.
  7. Keane, Michael, 1993. "Simulation estimation for panel data models with limited dependent variables," MPRA Paper 53029, University Library of Munich, Germany.
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