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Unit Averaging for Heterogeneous Panels

Christian Brownlees, Vladislav Morozov

arXiv 25 Oct 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 1 citations (OpenAlex)

arXiv:2210.14205 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this work we introduce a unit averaging procedure to efficiently recover unit-specific parameters in a heterogeneous panel model. The procedure consists in estimating the parameter of a given unit using a weighted average of all the unit-specific parameter estimators in the panel. The weights of the average are determined by minimizing an MSE criterion we derive. We analyze the properties of the resulting minimum MSE unit averaging estimator in a local heterogeneity framework inspired by the literature on frequentist model averaging, and we derive the local asymptotic distribution of the estimator and the corresponding weights. The benefits of the procedure are showcased with an application to forecasting unemployment rates for a panel of German regions.

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45
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1N. L. Hjort and G. Claeskens (2003) Frequentist Model Average Estimators1.00084100%
2N. Schanne, R. Wapler, and A. Weyh (2009) Regional Unemployment Forecasts with Spatial Interdependencies0.87452100%
3S. T. Buckland, K. P. Burnham, and N. H. Augustin (1997) Model Selection: An Integral Part of Inference0.84333100%
4C.-A. Liu (2015) Distribution Theory of the Least Squares Averaging Estimator0.81142100%
5B. E. Hansen (2015) Efficient shrinkage in parametric models0.73732100%
6G. S. Maddala, R. P. Trost, H. Li, and F. Joutz (1997) Estimation of Short-Run and Long-Run Elasticities of Energy Demand From Panel Data Using Shrinkage Estimators0.73732100%
7X. Zhang, G. Zou, and H. Liang (2014) Model averaging and weight choice in linear mixed-effects models0.73732100%
8W. Wang, X. Zhang, and R. Paap (2019) To pool or not to pool: What is a good strategy for parameter estimation and forecasting in panel regressions?0.69361100%
9L. Liu, H. R. Moon, and F. Schorfheide (2020) Forecasting with Dynamic Pane Data Models0.64441100%
10T. de Graaff, D. Arribas-Bel, and C. Ozgen (2018) Demographic Aging and Employment Dynamics in German Regions: Modeling Regional Heterogeneity0.64422100%

Showing the top 10 of 45 scored citations.