发表机构
HSE University（高等经济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个支持任意数量选择方程的多元样本选择泊松回归模型，推导了选择校正项，证明无排除限制的可识别性，并给出两步NLS与PPML估计量及稳健协方差矩阵；模拟显示PPML在相关误差下显著降低忽略选择导致的偏差且更稳定。
AI 中文摘要
本文发展了一个具有多元样本选择的泊松回归模型，在该模型中，仅当多个可能相关的选择条件得到满足时，结果变量才被观测到。据我们所知，这是第一个允许任意数量选择方程的泊松样本选择模型。我们在结果误差和选择误差服从联合正态分布的假设下推导了观测结果的条件均值，并得到了一个多元选择校正项。我们证明了模型参数的可识别性，并表明在适当的支撑条件和秩条件下，结果参数可以在没有排除限制的情况下被识别。我们还提出了基于非线性最小二乘和泊松伪最大似然的两步估计程序。据我们所知，本文是首次将PPML应用于具有样本选择的泊松回归模型。两种估计量的一致性均得到确立，并提出了一种稳健的两步夹心协方差矩阵，以考虑第一步选择模型带来的估计误差。此外，利用阶乘矩来恢复潜在结果误差的方差以及结果误差与选择误差之间的相关性。蒙特卡洛模拟表明，当结果误差与选择误差相关时，忽略样本选择会导致持续偏差，而所提出的PPML估计量能大幅减少这种偏差，并且比非线性最小二乘更稳定，尤其是在中等和较强的选择依赖性下。
英文摘要
This paper develops a Poisson regression model with multivariate sample selection, in which the outcome is observed only when several potentially correlated selection conditions are satisfied. To the best of our knowledge, this is the first Poisson sample selection model that allows for an arbitrary number of selection equations. We derive the conditional mean of the observed outcome under joint normality of the outcome and selection errors and obtain a multivariate selection-correction term. We prove identification of the model parameters and show that, under suitable support and rank conditions, the outcome parameters can be identified without an exclusion restriction. We also propose two-step estimation procedures based on nonlinear least squares and Poisson pseudo-maximum likelihood. To the best of our knowledge, this paper is the first to apply PPML to a Poisson regression model with sample selection. The consistency of both estimators is established, and a robust two-step sandwich covariance matrix is proposed to account for the estimation error from the first-step selection model. In addition, factorial moments are used to recover the variance of the latent outcome error and the correlations between the outcome and selection errors. Monte Carlo simulations show that ignoring sample selection leads to persistent bias when the outcome and selection errors are correlated, while the proposed PPML estimator substantially reduces this bias and is more stable than nonlinear least squares, especially under moderate and strong selection dependence.
Comments35 pages
