ORDINARY LEAST SQUARES ESTIMATION OF A DYNAMIC GAME MODEL

• 成果类型: Article
• 署名作者: Miessi Sanches, Fabio A.; Junior Silva, Daniel; Srisuma, Sorawoot
• 署名单位: Universidade de Sao Paulo; University of Warwick; University of Surrey
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12170
• 发表日期: 2016
• 页码: 623-633
• 关键词: Nonparametric identification; Sequential Estimation; structural models

摘要

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 a case. Our proposed estimator has a closed form. It can be computed without any numerical optimization and always minimizes the least squares objective function. We specify the optimally weighted GLS estimator that is efficient in the class of estimators under consideration. Our estimator appears to perform well in a simple Monte Carlo experiment.