Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper is concerned with bootstrap hypothesis testing in linear regression models with many regressors. I show that bootstrap F, LR and LM tests are asymptotically valid even when the numbers of estimated parameters and tested restrictions are not asymptotically negligible fractions of the sample size. One of the conditions for these results is that the regressors come from an asymptotically balanced design. Depending on the number of restrictions tested and on the errors’ distribution, violation of that condition might render the bootstrap tests asymptotically invalid. In that case, I propose bootstrapping Calhoun’s (2011) G statistic or modified versions of the LR and LM statistics, and show that these procedures remain asymptotically valid.