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Bibliographic Details
Main Author: Karl, Andrew T.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2412.20555
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author Karl, Andrew T.
author_facet Karl, Andrew T.
contents The Hausman specification test assesses the random-effects specification by comparing the random-effects estimator with a fixed-effects alternative. This note shows how a recently proposed bias diagnostic for linear mixed models can complement that test in random-effects panel-data applications. The diagnostic delivers parameter-specific internal estimates of finite-sample bias, together with permutation-based $p$-values, from a single fitted random-effects model. We illustrate its use in a gasoline-demand panel and in a value-added model for teacher evaluation using publicly available \textsf{R} packages, and we discuss how the resulting coefficient-specific bias summaries can be incorporated into routine practice.
format Preprint
id arxiv_https___arxiv_org_abs_2412_20555
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Parameter-Specific Bias Diagnostics in Random-Effects Panel Data Models
Karl, Andrew T.
Methodology
The Hausman specification test assesses the random-effects specification by comparing the random-effects estimator with a fixed-effects alternative. This note shows how a recently proposed bias diagnostic for linear mixed models can complement that test in random-effects panel-data applications. The diagnostic delivers parameter-specific internal estimates of finite-sample bias, together with permutation-based $p$-values, from a single fitted random-effects model. We illustrate its use in a gasoline-demand panel and in a value-added model for teacher evaluation using publicly available \textsf{R} packages, and we discuss how the resulting coefficient-specific bias summaries can be incorporated into routine practice.
title Parameter-Specific Bias Diagnostics in Random-Effects Panel Data Models
topic Methodology
url https://arxiv.org/abs/2412.20555