Many empirical studies estimate the structural effect of some variable on an outcome of interest while allowing for many covariates. We present inference methods that account for many covariates. The methods are based on asymptotics where the number of covariates grows as fast as the sample size. We find a limiting normal distribution with variance that is larger than the standard one. We also find that with homoskedasticity this larger variance can be accounted for by using degrees of freedom adjusted standard errors. We link this asymptotic theory to previous results for many instruments and for small bandwidths distributional approximations.
Alternative asymptotics and the partially linear model with many regressors
10 July 2015
Working Paper (CWP36/15)