This paper considers the estimation problem in dynamic games with finite actions. We derive the equation system that characterizes the Markovian equilibria. The equilibrium equation system enables us to characterize conditions for identification. We consider a class of asymptotic least squares estimators defined by the equilibrium conditions. This class provides a unified framework for a number of well-known estimators including those by Hotz and Miller (1993) and by Aguirregabiria and Mira (2002). We show that these estimators differ in the weight they assign to individual equilibrium conditions. We derive the efficient weight matrix. A Monte Carlo study illustrates the small sample performance and computational feasibility of alternative estimators. Copyright © 2008 The Review of Economic Studies Limited.
This paper studies the identification problem in infinite horizon Markovian games and proposes a generally applicable estimation method. Every period firms simultaneously select an action from a finite set. We characterize the set of Markov equilibria. Period profits are a linear function of equilibrium choice probabilities. The question of identification of these values is then reduced to the existence of a solution to this linear equation system. We characterize the identification conditions.We propose a simple estimation procedure which follows the steps in the identification argument.The estimator is consistent, asymptotic normally distributed, and efficient.We have collected quarterly time series data on pubs, restaurants, coffeehouses, bakeries and carpenters for two Austrian towns between 1982 and 2002. A dynamic entry game is estimated in which firms simultaneously decide whether to enter, remain active, or exit the industry. The period profit estimates are used to simulate the equilibrium behavior under a policy experiment in which a unit tax is imposed on firms deciding to enter the industry.
Limited information is the key element generating price dispersion in models of homogeneous-goods markets. We show that the global relationship between information and price dispersion is an inverse-U shape. We test this mechanism for the retail gasoline market using a new measure of information based on commuter data from Austria. Commuters sample gasoline prices on their commuting route, providing us with spatial variation in the share of informed consumers. Our empirical estimates are in line with the theoretical predictions. We also quantify how information affects average prices paid and the distribution of surplus in the gasoline market.
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