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The Structural Estimation of Behavioral Models: Discrete Choice Dynamic Programming Methods and Applications

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  • Keane, Michael P.
  • Todd, Petra E.
  • Wolpin, Kenneth I.

Abstract

The purpose of this chapter is twofold: (1) to provide an accessible introduction to the methods of structural estimation of discrete choice dynamic programming (DCDP) models and (2) to survey the contributions of applications of these methods to substantive and policy issues in labor economics. The first part of the chapter describes solution and estimation methods for DCDP models using, for expository purposes, a prototypical female labor force participation model. The next part reviews the contribution of the DCDP approach to three leading areas in labor economics: labor supply, job search and human capital. The final section discusses approaches to validating DCDP models.

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This chapter was published in:

  • O. Ashenfelter & D. Card (ed.), 2011. "Handbook of Labor Economics," Handbook of Labor Economics, Elsevier, edition 1, volume 4, number 4.
    This item is provided by Elsevier in its series Handbook of Labor Economics with number 4-04.

    Handle: RePEc:eee:labchp:4-04

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    Related research

    Keywords: Structural estimation; Discrete choice; Dynamic programming; Labor supply; Job search; Human capital;

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    2. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, Elsevier.
    3. Vivi Alatas & Abhijit Banerjee & Rema Hanna & Benjamin A. Olken & Ririn Purnamasari & Matthew Wai-Poi, 2013. "Ordeal Mechanisms In Targeting: Theory And Evidence From A Field Experiment In Indonesia," NBER Working Papers 19127, National Bureau of Economic Research, Inc.
    4. Peter Arcidiacono & Paul B. Ellickson, 2011. "Practical Methods for Estimation of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 363-394, 09.
    5. Andriy Norets & Xun Tang, 2013. "Semi-Parametric Inference in Dynamic Binary Choice Models," PIER Working Paper Archive 13-054, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    6. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    7. Yu Zheng & Juan Pantano, 2012. "Using Subjective Expectations Data to Allow for Unobserved Heterogeneity in Hotz-Miller Estimation Strategies," 2012 Meeting Papers 940, Society for Economic Dynamics.

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