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arXiv · 2007.11306

Two-Stage Regularization of Pseudo-Likelihood Estimators with Application to Time Series

Erez Buchweitz·Shlomo Ahal·Oded Papish·Guy Adini
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Abstract

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.

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Erez Buchweitz, Shlomo Ahal, Oded Papish, Guy Adini. 2020-07-22. Two-Stage Regularization of Pseudo-Likelihood Estimators with Application to Time Series. https://arxiv.org/abs/2007.11306

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