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 inSociological perspectives Vol. 63; no. 3; pp. 357 - 369
Main Authors Hill, Terrence D., Davis, Andrew P., Roos, J. Micah, French, Michael T.
Format Journal Article
LanguageEnglish
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.
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
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Snippet Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are...
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StartPage 357
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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