Premium
ORDINARY LEAST SQUARES ESTIMATION OF A DYNAMIC GAME MODEL
Author(s) -
Miessi Sanches Fabio A.,
Silva Daniel Junior,
Srisuma Sorawoot
Publication year - 2016
Publication title -
international economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.658
H-Index - 86
eISSN - 1468-2354
pISSN - 0020-6598
DOI - 10.1111/iere.12170
Subject(s) - estimator , ordinary least squares , mathematics , simple (philosophy) , least squares function approximation , mathematical optimization , minimax estimator , expression (computer science) , monte carlo method , stochastic game , function (biology) , computer science , statistics , minimum variance unbiased estimator , mathematical economics , philosophy , epistemology , evolutionary biology , biology , programming language
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.