Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/130056 
Year of Publication: 
2015
Series/Report no.: 
cemmap working paper No. CWP36/15
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Subjects: 
non-standard asymptotics
partially linear model
many terms
adjusted variance
JEL: 
C13
C31
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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