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Ordinary Least Squares Estimation for a Dynamic Game

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  • Fabio A. Miessi Sanches
  • Daniel Silva Junior, Sorawoot Srisuma

Abstract

Estimation of dynamic games is known to be a numerically challenging task. A common form of the payoff functions employed in practice takes the linear-in-parameter specification. We show a least squares estimator taking a familiar OLS/GLS expression is available in such case. Our proposed estimator has a closed-form. It can be computed without any numerical optimization and always minimizes the least squares objective function. Our estimator is also asymptotically equivalent to the asymptotic least squares estimator of Pesendorfer and Schmidt-Dengler (2008). Our estimator appears to perform well in a simple Monte Carlo experiment.

Suggested Citation

  • Fabio A. Miessi Sanches & Daniel Silva Junior, Sorawoot Srisuma, 2014. "Ordinary Least Squares Estimation for a Dynamic Game," Working Papers, Department of Economics 2014_19, University of São Paulo (FEA-USP), revised 23 Feb 2015.
    • Handle: RePEc:spa:wpaper:2014wpecon19
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    References listed on IDEAS

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    Cited by:

    1. Fabio Sanches & Daniel Silva Junior & Sorawoot Srisuma, 2018. "Minimum Distance Estimation of Search Costs Using Price Distribution," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 658-671, October.

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    More about this item

    Keywords

    Closed-from Estimation; Dynamic Discrete Choice; Markovian Games;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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