TY - JOUR
T1 - Limitations of Fixed-Effects Models for Panel Data
AU - Hill, Terrence D.
AU - Davis, Andrew P.
AU - Roos, J. Micah
AU - French, Michael T.
N1 - Funding Information:
This work was supported by an Agriculture and Food Research Initiative (AFRI) competitive grant 2013-67013-21232 from the USDA National Institute of Food and Agriculture (NIFA). We thank Maryann Fink, Nick Vail, Emily Rodekohr, Steve McKay, Brian Leckie, and William Holdsworth for assistance with field, greenhouse, and wet lab work. We would also like to thank Jessica Rutkoski for her invaluable feedback on paper drafts and consultation throughout the course of this experiment. Further, the authors thank the University of Wisconsin Biotechnology Center DNA Sequencing Facility, as well as the Weill Cornell Medicine Core Laboratories Center and the Cornell Biotechnology Resource Center for providing sequencing/ genotyping facilities and services. The authors declare no conflict of interest. MM is a cofounder of Row 7 Seed Company but does not receive compensation nor holds equity.
Publisher Copyright:
© The Author(s) 2019.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - critique
KW - fixed-effects
KW - limitations
KW - longitudinal
KW - panel data
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U2 - 10.1177/0731121419863785
DO - 10.1177/0731121419863785
M3 - Article
AN - SCOPUS:85070264378
SN - 0731-1214
VL - 63
SP - 357
EP - 369
JO - Sociological Perspectives
JF - Sociological Perspectives
IS - 3
ER -