论文标题
伪样估计器的两阶段正则化，并应用时间序列
Two-Stage Regularization of Pseudo-Likelihood Estimators with Application to Time Series
论文作者
论文摘要
从非分数函数得出的估计器在实用和现代应用中广泛使用。它们的正则化通常是通过伪后验估计来进行的，这是通过对分数函数增加惩罚的等效性。我们认为，这种方法是次优的，并提出了一个两阶段的替代方案，涉及估计新的分数函数，该函数可以更好地近似于正规化目的的真实可能性。我们的方法通常以最大的A型估计来识别原始分数函数实际上是可能性的。我们将理论应用于同时的外生性下适合普通的最小二乘（OLS），这是一个经常出现在时间序列中的环境，其中OLS是从业者选择的估计器。
Estimators derived from score functions that are not the likelihood are in wide use in practical and modern applications. Their regularization is often carried by pseudo-posterior estimation, equivalently by adding penalty to the score function. We argue that this approach is suboptimal, and propose a two-staged alternative involving estimation of a new score function which better approximates the true likelihood for the purpose of regularization. Our approach typically identifies with maximum a-posteriori estimation if the original score function is in fact the likelihood. We apply our theory to fitting ordinary least squares (OLS) under contemporaneous exogeneity, a setting appearing often in time series and in which OLS is the estimator of choice by practitioners.
