The common practice to include firm fixed effects in empirical research may eliminate the explanatory power of important economic factors that are persistent. To illustrate this point, we review the intuitive R&D-patent relation in recent studies and present a surprising pattern that R&D input only positively explains patent output in half of prior regression estimations. This “missing link” can be attributed to the persistence of R&D and patents that causes the between-firm variation to be absorbed by firm fixed effects. We consider modified Hausman-Taylor estimates and advanced machine learning methods, and find that both methods lead to a consistent positive R&D-patent relation. In particular, the advanced machine learning methods select some firm dummies that have explanatory power for patent output, and exclude other firm dummies are not informative for – and may even bias – the identification. This paper thus offers two methods to serve as a “second opinion” for empirical researchers working with explanatory variables that strongly correlate with between-individual unobservables.
Ansprechpartner: Daehyun Kim
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