无转移支付的多对一双边匹配中的识别与估计
Identification and Estimation in Many-to-one Two-sided Matching without Transfers
AI总结：
本文研究无转移支付的多对一双边匹配中双方偏好的识别问题，在稳定性假设和排除性限制下证明非参数识别，并通过蒙特卡洛模拟验证参数贝叶斯方法在现实规模问题中的有效性，最后以智利学校录取数据为例进行反事实政策分析。
摘要：
在非转移效用下的多对一双边匹配环境中（例如大学录取），我们研究了在仅有一个市场数据的情况下识别双方偏好的条件。无论市场是集中式还是分散式，在假设观测到的匹配是稳定的前提下，我们证明了在某些排除性限制条件下双方偏好的非参数识别。为了将结果应用于数据，我们使用蒙特卡洛模拟来评估不同的估计量，包括直接从识别结果构造的估计量。我们发现，带有吉布斯采样器的参数贝叶斯方法在现实规模的问题中表现良好。最后，我们以智利公立和私立学校的分散式录取为例说明了我们的方法，并对一项平权行动政策进行了反事实分析。
英文摘要：
In a setting of many-to-one two-sided matching with non-transferable utilities, e.g., college admissions, we study conditions under which preferences of both sides are identified with data on one single market. Regardless of whether the market is centralized or decentralized, assuming that the observed matching is stable, we show nonparametric identification of preferences of both sides under certain exclusion restrictions. To take our results to the data, we use Monte Carlo simulations to evaluate different estimators, including the ones that are directly constructed from the identification. We find that a parametric Bayesian approach with a Gibbs sampler works well in realistically sized problems. Finally, we illustrate our methodology in decentralized admissions to public and private schools in Chile and conduct a counterfactual analysis of an affirmative action policy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
