AffiliationUniv Arizona, Dept Econ
MetadataShow full item record
PublisherAMER STATISTICAL ASSOC
CitationMurillo Campello, Antonio F. Galvao & Ted Juhl (2019) Testing for Slope Heterogeneity Bias in Panel Data Models, Journal of Business & Economic Statistics, 37:4, 749-760, DOI: 10.1080/07350015.2017.1421545
RightsCopyright © 2019 American Statistical Association Journal of Business & Economic Statistics
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractStandard econometric methods can overlook individual heterogeneity in empirical work, generating inconsistent parameter estimates in panel data models. We propose the use of methods that allow researchers to easily identify, quantify, and address estimation issues arising from individual slope heterogeneity. We first characterize the bias in the standard fixed effects estimator when the true econometric model allows for heterogeneous slope coefficients. We then introduce a new test to check whether the fixed effects estimation is subject to heterogeneity bias. The procedure tests the population moment conditions required for fixed effects to consistently estimate the relevant parameters in the model. We establish the limiting distribution of the test and show that it is very simple to implement in practice. Examining firm investment models to showcase our approach, we show that heterogeneity bias-robust methods identify cash flow as a more important driver of investment than previously reported. Our study demonstrates analytically, via simulations, and empirically the importance of carefully accounting for individual specific slope heterogeneity in drawing conclusions about economic behavior.
Note12 month embargo; published online: 18 June 2018
VersionFinal accepted manuscript