论文标题
在有限样品中测试动态离散游戏中的同质性
Testing homogeneity in dynamic discrete games in finite samples
论文作者
论文摘要
关于动态离散游戏的文献通常假定有条件的选择概率和国家过渡概率在整个市场和随着时间的推移之间都是均匀的。我们将其称为动态离散游戏中的“同质性假设”。该假设使经验研究能够通过汇总来自多个市场和许多时间段内的数据来估计游戏的结构参数。在本文中，我们提出了一个假设检验，以评估数据中是否存在同质性假设。我们的假设检验是通过马尔可夫链蒙特卡洛（MCMC）算法实现的近似随机测试的结果。我们表明，由于（用户定义的）MCMC数量差异，对于任何固定数量的市场，时间段和参与者，我们的假设测试变得有效。我们将测试应用于瑞安（Ryan）美国波特兰水泥业的实证研究（2012年）。
The literature on dynamic discrete games often assumes that the conditional choice probabilities and the state transition probabilities are homogeneous across markets and over time. We refer to this as the "homogeneity assumption" in dynamic discrete games. This assumption enables empirical studies to estimate the game's structural parameters by pooling data from multiple markets and from many time periods. In this paper, we propose a hypothesis test to evaluate whether the homogeneity assumption holds in the data. Our hypothesis test is the result of an approximate randomization test, implemented via a Markov chain Monte Carlo (MCMC) algorithm. We show that our hypothesis test becomes valid as the (user-defined) number of MCMC draws diverges, for any fixed number of markets, time periods, and players. We apply our test to the empirical study of the U.S.\ Portland cement industry in Ryan (2012).
