Alternative asymptotics and the partially linear model with many regressors

Matias Cattaneo, Michael Jansson and Whitney Newey

No CWP36/15, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Keywords: Non-standard asymptotics; partially linear model; many terms; adjusted variance (search for similar items in EconPapers)
JEL-codes: C13 C31 (search for similar items in EconPapers)
Date: 2015-07-10
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS (2018)
Working Paper: Alternative Asymptotics and the Partially Linear Model with Many Regressors (2015)
Working Paper: Alternative asymptotics and the partially linear model with many regressors (2015)
Working Paper: Alternative Asymptotics and the Partially Linear Model with Many Regressors (2012)
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