Identification and estimation of panel semiparametric conditional heteroskedastic frontiers with dynamic inefficiency
研究了一个面板半参数随机前沿模型，允许时变条件异方差生产率成分，利用Kotlarski引理的条件特征函数推导新矩条件，识别无效率和随机噪声的异方差方差参数，并开发了非参数估计方法。
.We study a semiparametric panel stochastic frontier model with nonseparable unobserved heterogeneity, which allows for time-varying conditional heteroskedastic productivity components. It does not require distributional assumptions on random noise except conditional symmetry. We utilize conditional characteristic functions from Kotlarski’s Lemma to derive new moment conditions that yield the identification of the heteroskedastic variance parameters of inefficiency and random noise. Identification only requires a panel with three periods for serially correlated inefficiency. A nonparametric estimation procedure is also developed for the conditional variance of inefficiency, and its convergence rate is established. Monte Carlo simulation shows that the estimator is robust to misspecification of inefficiency distributions.