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Identification of Counterfactuals in Dynamic Discrete Choice Models

Author

Listed:
  • Myrto Kalouptsidi
  • Paul T. Scott
  • Eduardo Souza-Rodrigues
Abstract
Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non-identification of models does not necessarily imply the non-identification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non- additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry/exit problem numerically, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and non-identified counterfactuals of interest.

Suggested Citation

  • Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals in Dynamic Discrete Choice Models," NBER Working Papers 21527, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:21527
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    2. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    3. Aguirregabiria, Victor & Gu, Jiaying & Luo, Yao, 2021. "Sufficient statistics for unobserved heterogeneity in structural dynamic logit models," Journal of Econometrics, Elsevier, vol. 223(2), pages 280-311.
    4. Pamela Giustinelli & Matthew D. Shapiro, 2024. "SeaTE: Subjective Ex Ante Treatment Effect of Health on Retirement," American Economic Journal: Applied Economics, American Economic Association, vol. 16(2), pages 278-317, April.
    5. De Groote, Olivier, 2019. "Dynamic Effort Choice in High School: Costs and Benefits of an Academic Track," TSE Working Papers 19-1002, Toulouse School of Economics (TSE), revised Jun 2023.
    6. Jaap H. Abbring & Øystein Daljord, 2020. "Identifying the discount factor in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 11(2), pages 471-501, May.
    7. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2020. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," Working Papers tecipa-674, University of Toronto, Department of Economics.
    8. Kalouptsidi, Myrto, 2017. "Detection and Impact of Industrial Subsidies: The Case of Chinese Shipbuilding," CEPR Discussion Papers 12080, C.E.P.R. Discussion Papers.
    9. Giulia Brancaccio & Myrto Kalouptsidi & Theodore Papageorgiou, 2020. "Geography, Transportation, and Endogenous Trade Costs," Econometrica, Econometric Society, vol. 88(2), pages 657-691, March.
    10. Joachim Freyberger, 2021. "Normalizations and misspecification in skill formation models," Papers 2104.00473, arXiv.org, revised Jul 2022.
    11. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," CEPR Discussion Papers 13240, C.E.P.R. Discussion Papers.
    12. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," NBER Working Papers 25134, National Bureau of Economic Research, Inc.
    13. Nikhil Agarwal & Itai Ashlagi & Michael A. Rees & Paulo Somaini & Daniel Waldinger, 2021. "Equilibrium Allocations Under Alternative Waitlist Designs: Evidence From Deceased Donor Kidneys," Econometrica, Econometric Society, vol. 89(1), pages 37-76, January.
    14. Nikhil Agarwal & Itai Ashlagi & Michael A. Rees & Paulo J. Somaini & Daniel C. Waldinger, 2019. "Equilibrium Allocations under Alternative Waitlist Designs: Evidence from Deceased Donor Kidneys," NBER Working Papers 25607, National Bureau of Economic Research, Inc.

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture

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