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• W3100302698 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." @default.
• W3100302698 created "2020-11-23" @default.
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• W3100302698 date "2020-07-22" @default.
• W3100302698 modified "2023-09-27" @default.
• W3100302698 title "Two-Stage Regularization of Pseudo-Likelihood Estimators with Application to Time Series" @default.
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• W3100302698 hasPublicationYear "2020" @default.
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