Ordinary Least Squares Estimation for a Dynamic Game
Fabio A. Miessi Sanches () and
No 2014_19, Working Papers, Department of Economics from University of São Paulo (FEA-USP)
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.
Keywords: Closed-from Estimation; Dynamic Discrete Choice; Markovian Games (search for similar items in EconPapers)
JEL-codes: C14 C25 C61 (search for similar items in EconPapers)
Date: 2014-10-16, Revised 2015-02-23
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spa:wpaper:2014wpecon19
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in Working Papers, Department of Economics from University of São Paulo (FEA-USP) Contact information at EDIRC.
Bibliographic data for series maintained by Pedro Garcia Duarte ().