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Identifying the discount factor in dynamic discrete choice models

Author

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  • Jaap H. Abbring
  • Øystein Daljord
Abstract
Empirical research often cites observed choice responses to variation that shifts expected discounted future utilities, but not current utilities, as an intuitive source of information on time preferences. We study the identification of dynamic discrete choice models under such economically motivated exclusion restrictions on primitive utilities. We show that each exclusion restriction leads to an easily interpretable moment condition with the discount factor as the only unknown parameter. The identified set of discount factors that solves this condition is finite, but not necessarily a singleton. Consequently, in contrast to common intuition, an exclusion restriction does not in general give point identification. Finally, we show that exclusion restrictions have nontrivial empirical content: The implied moment conditions impose restrictions on choices that are absent from the unconstrained model.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:quante:v:11:y:2020:i:2:p:471-501
    DOI: 10.3982/QE1352
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    6. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    7. Doug J. Chung & Byungyeon Kim & Byoung G. Park, 2021. "The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training," Management Science, INFORMS, vol. 67(11), pages 7046-7074, November.
    8. Murasawa, Yasutomo, 2023. "大学中退の逐次意思決定モデルの構造推定 [Structural estimation of a sequential decision model of college dropout]," MPRA Paper 118183, University Library of Munich, Germany.
    9. An, Yonghong & Hu, Yingyao & Xiao, Ruli, 2021. "Dynamic decisions under subjective expectations: A structural analysis," Journal of Econometrics, Elsevier, vol. 222(1), pages 645-675.
    10. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza‐Rodrigues, 2021. "Identification of counterfactuals in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 12(2), pages 351-403, May.
    11. Øystein Daljord, 2022. "Durable Goods Adoption and the Consumer Discount Factor: A Case Study of the Norwegian Book Market," Management Science, INFORMS, vol. 68(9), pages 6783-6796, September.
    12. Cheng Chou & Geert Ridder & Ruoyao Shi, 2024. "Identification and Estimation of Nonstationary Dynamic Binary Choice Models," Working Papers 202402, University of California at Riverside, Department of Economics.
    13. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    14. Schiraldi, Pasquale & Levy, Matthew R., 2021. "Identification of Dynamic Discrete-Continuous Choice Models, with an Application to Consumption-Savings-Retirement," CEPR Discussion Papers 15719, C.E.P.R. Discussion Papers.
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    16. Schneider, Ulrich, 2019. "Identification of Time Preferences in Dynamic Discrete Choice Models: Exploiting Choice Restrictions," MPRA Paper 102137, University Library of Munich, Germany, revised 29 Jul 2020.
    17. D. Nishijima & M. Oguchi, 2024. "Comparing Product Lifetime Extensions by Enhancing Consumers’ Expected Product Lifetime Among Different Durable Products," Journal of Consumer Policy, Springer, vol. 47(2), pages 223-239, June.
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    19. Ben Deaner, 2020. "Approximation-Robust Inference in Dynamic Discrete Choice," Papers 2010.11482, arXiv.org.

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