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Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models

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Listed:
  • Myrto Kalouptsidi
  • Paul T. Scott
  • Eduardo Souza-Rodrigues

Abstract

In structural dynamic discrete choice models, unobserved or mis-measured state variables may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address such measurement problems when they relate to state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent's entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computationally light. We provide constructive arguments for the identification of model primitives, and establish the estimator's consistency and asymptotic normality. Four applied examples serve to illustrate the ECCP approach's implementation, advantages, and limitations: dynamic demand for durable goods, agricultural land use change, technology adoption, and dynamic labor supply. We illustrate the estimator's good finite-sample performance in a Monte Carlo study, and we estimate a labor supply model empirically for taxi drivers in New York City.

Suggested Citation

  • 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.
  • Handle: RePEc:tor:tecipa:tecipa-674
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    References listed on IDEAS

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    1. Horváth, Lajos & Yandell, Brian S., 1988. "Asymptotics of conditional empirical processes," Journal of Multivariate Analysis, Elsevier, vol. 26(2), pages 184-206, August.
    2. Olivier De Groote & Frank Verboven, 2019. "Subsidies and Time Discounting in New Technology Adoption: Evidence from Solar Photovoltaic Systems," American Economic Review, American Economic Association, vol. 109(6), pages 2137-2172, June.
    3. Ransom, Tyler, 2019. "Labor Market Frictions and Moving Costs of the Employed and Unemployed," IZA Discussion Papers 12139, Institute of Labor Economics (IZA).
    4. 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.
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    Cited by:

    1. Steven T. Berry & Giovanni Compiani, 2020. "An Instrumental Variable Approach to Dynamic Models," Working Papers 2020-106, Becker Friedman Institute for Research In Economics.
    2. Steven T. Berry & Giovanni Compiani, 2020. "An Instrumental Variable Approach to Dynamic Models," NBER Working Papers 27756, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    dynamic discrete choice; unobserved states; instrumental variables; identification; Euler equations;
    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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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