Limitations of Fixed-Effects Models for Panel Data
Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external va...
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Published in | Sociological perspectives Vol. 63; no. 3; pp. 357 - 369 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Los Angeles, CA
Sage Publications, Inc
01.06.2020
SAGE Publications SAGE PUBLICATIONS, INC |
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Abstract | Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students. |
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AbstractList | Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students. |
Author | Davis, Andrew P. Hill, Terrence D. French, Michael T. Roos, J. Micah |
Author_xml | – sequence: 1 givenname: Terrence D. surname: Hill fullname: Hill, Terrence D. – sequence: 2 givenname: Andrew P. surname: Davis fullname: Davis, Andrew P. – sequence: 3 givenname: J. Micah surname: Roos fullname: Roos, J. Micah – sequence: 4 givenname: Michael T. surname: French fullname: French, Michael T. |
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References | Allison, Williams, Moral-Benito 2017; 3 French, Popovici 2011; 20 Nickell 1981; 49 Beck, Katz 2001; 55 Hsiao 1985; 4 DeMaris 2014; 19 Bell, Jones 2015; 3 Hausman 1978; 46 Sobel 2012; 107 Vaisey, Miles 2017; 46 bibr10-0731121419863785 bibr15-0731121419863785 bibr18-0731121419863785 bibr1-0731121419863785 bibr9-0731121419863785 bibr3-0731121419863785 Treiman Donald (bibr17-0731121419863785) 2009 bibr5-0731121419863785 Fox John (bibr7-0731121419863785) 2016 bibr16-0731121419863785 bibr2-0731121419863785 bibr8-0731121419863785 bibr6-0731121419863785 Wooldridge Jeffrey (bibr19-0731121419863785) 2010 bibr4-0731121419863785 Longhi Simonetta (bibr14-0731121419863785) 2014 bibr11-0731121419863785 |
References_xml | – volume: 49 start-page: 1417 year: 1981 article-title: Biases in Dynamic Models with Fixed Effects publication-title: Econometrica contributor: fullname: Nickell – volume: 3 start-page: 1 year: 2017 end-page: 17 article-title: Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects publication-title: Socius contributor: fullname: Moral-Benito – volume: 19 start-page: 380 year: 2014 article-title: Combating Unmeasured Confounding in Cross-sectional Studies: Evaluating Instrumental-variable and Heckman Selection Models publication-title: Psychological Methods contributor: fullname: DeMaris – volume: 3 start-page: 133 year: 2015 article-title: Explaining Fixed Effects: Random Effects Modeling of Time-series Cross-sectional and Panel Data publication-title: Political Science Research and Methods contributor: fullname: Jones – volume: 46 start-page: 1251 year: 1978 article-title: Specification Tests in Econometrics publication-title: Econometrica contributor: fullname: Hausman – volume: 55 start-page: 487 year: 2001 article-title: Throwing Out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon publication-title: International Organization contributor: fullname: Katz – volume: 46 start-page: 44 year: 2017 end-page: 67 article-title: What You Can—and Can’t—Do with Three-wave Panel Data publication-title: Sociological Methods & Research contributor: fullname: Miles – volume: 20 start-page: 127 year: 2011 article-title: That Instrument Is Lousy! In Search of Agreement When Using Instrumental Variables Estimation in Substance Use Research publication-title: Health Economics contributor: fullname: Popovici – volume: 4 start-page: 121 year: 1985 article-title: Benefits and Limitations of Panel Data publication-title: Econometric Reviews contributor: fullname: Hsiao – volume: 107 start-page: 521 year: 2012 article-title: Does Marriage Boost Men’s Wages? Identification of Treatment Effects in Fixed Effects Regression Models for Panel Data publication-title: Journal of the American Statistical Association contributor: fullname: Sobel – ident: bibr3-0731121419863785 doi: 10.1515/9781400829828 – ident: bibr1-0731121419863785 doi: 10.4135/9781412993869 – ident: bibr10-0731121419863785 doi: 10.2307/1913827 – ident: bibr16-0731121419863785 doi: 10.1080/01621459.2011.646917 – ident: bibr5-0731121419863785 doi: 10.1017/psrm.2014.7 – ident: bibr18-0731121419863785 doi: 10.1177/0049124114547769 – ident: bibr6-0731121419863785 doi: 10.1037/a0037416 – ident: bibr8-0731121419863785 doi: 10.1002/hec.1572 – ident: bibr11-0731121419863785 doi: 10.1080/07474938508800078 – volume-title: Applied Regression Analysis and Generalized Linear Models year: 2016 ident: bibr7-0731121419863785 contributor: fullname: Fox John – volume-title: Econometric Analysis of Cross Section and Panel Data year: 2010 ident: bibr19-0731121419863785 contributor: fullname: Wooldridge Jeffrey – volume-title: Quantitative Data Analysis: Doing Social Research to Test Ideas year: 2009 ident: bibr17-0731121419863785 contributor: fullname: Treiman Donald – ident: bibr9-0731121419863785 – volume-title: A Practical Guide to Using Panel Data year: 2014 ident: bibr14-0731121419863785 contributor: fullname: Longhi Simonetta – ident: bibr4-0731121419863785 doi: 10.1162/00208180151140658 – ident: bibr15-0731121419863785 doi: 10.2307/1911408 – ident: bibr2-0731121419863785 doi: 10.1177/2378023117710578 |
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SubjectTerms | Bias Causality Data analysis Editor's Pick Heterogeneity Limitations Longitudinal studies Measurement Measurement errors Panel data Statistical power Time periods |
Title | Limitations of Fixed-Effects Models for Panel Data |
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