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Interpretation and identification of within-unit and cross-sectional variation in panel data models

While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis. In addition, we show through novel mathematical decomposition and simulation that only...

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Detalles Bibliográficos
Autores principales: Kropko, Jonathan, Kubinec, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173782/
https://www.ncbi.nlm.nih.gov/pubmed/32315338
http://dx.doi.org/10.1371/journal.pone.0231349
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author Kropko, Jonathan
Kubinec, Robert
author_facet Kropko, Jonathan
Kubinec, Robert
author_sort Kropko, Jonathan
collection PubMed
description While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis. In addition, we show through novel mathematical decomposition and simulation that only one-way FE models cleanly capture either the over-time or cross-sectional dimensions in panel data, while the two-way FE model unhelpfully combines within-unit and cross-sectional variation in a way that produces un-interpretable answers. In fact, as we show in this paper, if we begin with the interpretation that many researchers wrongly assign to the two-way FE model—that it represents a single estimate of X on Y while accounting for unit-level heterogeneity and time shocks—the two-way FE specification is statistically unidentified, a fact that statistical software packages like R and Stata obscure through internal matrix processing.
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spelling pubmed-71737822020-04-27 Interpretation and identification of within-unit and cross-sectional variation in panel data models Kropko, Jonathan Kubinec, Robert PLoS One Research Article While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis. In addition, we show through novel mathematical decomposition and simulation that only one-way FE models cleanly capture either the over-time or cross-sectional dimensions in panel data, while the two-way FE model unhelpfully combines within-unit and cross-sectional variation in a way that produces un-interpretable answers. In fact, as we show in this paper, if we begin with the interpretation that many researchers wrongly assign to the two-way FE model—that it represents a single estimate of X on Y while accounting for unit-level heterogeneity and time shocks—the two-way FE specification is statistically unidentified, a fact that statistical software packages like R and Stata obscure through internal matrix processing. Public Library of Science 2020-04-21 /pmc/articles/PMC7173782/ /pubmed/32315338 http://dx.doi.org/10.1371/journal.pone.0231349 Text en © 2020 Kropko, Kubinec http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kropko, Jonathan
Kubinec, Robert
Interpretation and identification of within-unit and cross-sectional variation in panel data models
title Interpretation and identification of within-unit and cross-sectional variation in panel data models
title_full Interpretation and identification of within-unit and cross-sectional variation in panel data models
title_fullStr Interpretation and identification of within-unit and cross-sectional variation in panel data models
title_full_unstemmed Interpretation and identification of within-unit and cross-sectional variation in panel data models
title_short Interpretation and identification of within-unit and cross-sectional variation in panel data models
title_sort interpretation and identification of within-unit and cross-sectional variation in panel data models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173782/
https://www.ncbi.nlm.nih.gov/pubmed/32315338
http://dx.doi.org/10.1371/journal.pone.0231349
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