Shrinkage for Categorical Regressors
Journal
Journal of Econometrics
ISSN
0304-4076
Type
journal article
Date Issued
2021-07
Author(s)
Abstract
This paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared l2-norm differences between group location parameters and informative first stage estimates. Under quadratic loss, the penalized estimation problem has a simple interpretable closed-form solution that nests methods established in the literature on ridge regression, discretized support smoothing kernels and model averaging methods. We derive risk-optimal penalty parameters and propose a plug-in approach for estimation. The large sample properties are analyzed in an asymptotic local to zero framework by introducing a class of sequences for close and distant systems of locations that is sufficient for describing a large range of data generating processes. We provide the asymptotic distributions of the shrinkage estimators under different penalization schemes. The proposed plug-in estimator uniformly dominates the ordinary least squares estimator in terms of asymptotic risk if the number of groups is larger than three. Monte Carlo simulations reveal robust improvements over standard methods in finite samples. Real data examples of estimating time trends in a panel and a difference-in-differences study illustrate potential applications.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Economic Policy
Refereed
Yes
Publisher
North-Holland
Volume
223
Number
1
Start page
161
End page
189
Pages
29
Subject(s)
Eprints ID
264189
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