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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7173782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kropkojonathan interpretationandidentificationofwithinunitandcrosssectionalvariationinpaneldatamodels AT kubinecrobert interpretationandidentificationofwithinunitandcrosssectionalvariationinpaneldatamodels |