Limitations of Fixed-Effects Models for Panel Data
By Terrence D. Hill, Andrew P. Davis, J. Roos, Michael T French. Sociological perspectives, 2020
296 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Health, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1a9974e623287867c6debb2c794df207305715ff
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Citations in the corpus
- Circular Economy Innovation and Environmental Sustainability Impact on Economic Growth: An Integrated Model for Sustainable Development (2020, methodology): “If the fixed effect method is a widely used method, but sometimes criticized [22], the GMM, using the Arellano–Bond conditions, represent a robust method instead [23].”