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Dynamic policy analysis

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  • Jaap Abbring

    (Institute for Fiscal Studies and Tinbergen Institute)

  • James Heckman

    (Institute for Fiscal Studies and University of Chicago)

Abstract

This chapter studies the microeconometric treatment-effect and structural approaches to dynamic policy evaluation. First, we discuss a reduced-form approach based on a sequential randomization or dynamic matching assumption that is popular in biostatistics. We then discuss two complementary approaches for treatments that are single stopping times and that allow for non- trivial dynamic selection on unobservables. The first builds on continuous-time duration and event-history models. The second extends the discrete-time dynamic discrete-choice literature.

Suggested Citation

  • Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:05/08
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    Cited by:

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    2. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    3. Mroz, T. & Picone, G., 2011. "A Multiple State Duration Model with Endogenous Treatment," Health, Econometrics and Data Group (HEDG) Working Papers 11/19, HEDG, c/o Department of Economics, University of York.
    4. Majumdar, Sumit K., 2014. "Technology and wages: Why firms invest and what happens," Technology in Society, Elsevier, vol. 39(C), pages 44-54.
    5. Majumdar, Sumit K., 2016. "Debt and communications technology diffusion: Retrospective evidence," Research Policy, Elsevier, vol. 45(2), pages 458-474.
    6. Majumdar, Sumit K., 2016. "R&D and the overseas earnings of Indian firms," Technology in Society, Elsevier, vol. 44(C), pages 104-111.

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