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Estimation of Dynamic Discrete Choice Models in Continuous Time

Author

Listed:
  • Peter Arcidiacono
  • Patrick J. Bayer
  • Jason R. Blevins
  • Paul Ellickson
Abstract
This paper provides a method for estimating large-scale dynamic discrete choice models (in both single- and multi-agent settings) within a continuous time framework. The advantage of working in continuous time is that state changes occur sequentially, rather than simultaneously, avoiding a substantial curse of dimensionality that arises in multi-agent settings. Eliminating this computational bottleneck is the key to providing a seamless link between estimating the model and performing post-estimation counterfactuals. While recently developed two-step estimation techniques have made it possible to estimate large-scale problems, solving for equilibria remains computationally challenging. In many cases, the models that applied researchers estimate do not match the models that are then used to perform counterfactuals. By modeling decisions in continuous time, we are able to take advantage of the recent advances in estimation while preserving a tight link between estimation and policy experiments. We also consider estimation in situations with imperfectly sampled data, such as when we do not observe the decision not to move, or when data is aggregated over time, such as when only discrete-time data are available at regularly spaced intervals. We illustrate the speed and scope of our framework using several large-scale Monte Carlo experiments.

Suggested Citation

  • Peter Arcidiacono & Patrick J. Bayer & Jason R. Blevins & Paul Ellickson, 2010. "Estimation of Dynamic Discrete Choice Models in Continuous Time," Working Papers 10-49, Duke University, Department of Economics.
    • Handle: RePEc:duk:dukeec:10-49
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    References listed on IDEAS

    as
    1. Peter Arcidiacono, 2005. "Affirmative Action in Higher Education: How Do Admission and Financial Aid Rules Affect Future Earnings?," Econometrica, Econometric Society, vol. 73(5), pages 1477-1524, September.
    2. Caplin, Andrew & Nalebuff, Barry, 1991. "Aggregation and Imperfect Competition: On the Existence of Equilibrium," Econometrica, Econometric Society, vol. 59(1), pages 25-59, January.
    3. Victor Aguirregabiria & Pedro Mira, 2007. "Sequential Estimation of Dynamic Discrete Games," Econometrica, Econometric Society, vol. 75(1), pages 1-53, January.
    4. Robert Miller & Peter Arcidiacono, 2008. "CCP Estimation of Dynamic Discrete Choice Models with Unobserved Heterogeneity," 2008 Meeting Papers 1065, Society for Economic Dynamics.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Nicolai V. Kuminoff & V. Kerry Smith & Christopher Timmins, 2013. "The New Economics of Equilibrium Sorting and Policy Evaluation Using Housing Markets," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1007-1062, December.
    2. Otsu, Taisuke & Pesendorfer, Martin & Takahashi, Yuya, 2013. "Testing for equilibrium multiplicity in dynamic Markov games," LSE Research Online Documents on Economics 101968, London School of Economics and Political Science, LSE Library.
    3. Nicolai V. Kuminoff & V. Kerry Smith & Christopher Timmins, 2010. "The New Economics of Equilibrium Sorting and its Transformational Role for Policy Evaluation," NBER Working Papers 16349, National Bureau of Economic Research, Inc.
    4. Takahashi, Yuya, 2013. "Estimating a War of Attrition: The Case of the U.S. Movie Theater Industry," Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 424, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
    5. 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, September.
    6. Doraszelski, Ulrich & Escobar, Juan F., 2019. "Protocol invariance and the timing of decisions in dynamic games," Theoretical Economics, Econometric Society, vol. 14(2), May.
    7. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory & Urzua, Sergio, 2014. "Education, Health and Wages," IZA Discussion Papers 8027, Institute of Labor Economics (IZA).
    8. Taisuke Otsu & Martin Pesendorfer & Yuya Takahashi, 2016. "Pooling data across markets in dynamic Markov games," Quantitative Economics, Econometric Society, vol. 7(2), pages 523-559, July.
    9. Hong, Han & Li, Weiming & Wang, Boyu, 2015. "Estimation of dynamic discrete models from time aggregated data," Journal of Econometrics, Elsevier, vol. 188(2), pages 435-446.
    10. Pesendorfer, Martin & Takahashi, Yuya & Otsu, Taisuke, 2014. "Testing Equilibrium Multiplicity in Dynamic Games," CEPR Discussion Papers 10111, C.E.P.R. Discussion Papers.
    11. Victor Aguirregabiria & Junichi Suzuki, 2015. "Empirical Games of Market Entry and Spatial Competition in Retail Industries," Working Papers tecipa-534, University of Toronto, Department of Economics.
    12. Andrew Sweeting, 2015. "A Model of Non-Stationary Dynamic Price Competition with an Application to Platform Design," Working Papers 15-03, NET Institute.

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    More about this item

    Keywords

    Dynamic Discrete Choice; Discrete Dynamic Games; Continuous Time;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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