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ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS

Author(s)
Cattaneo, Matias D.; Jansson, Michael; Newey, Whitney K
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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 bandwidth(s) distributional approximations. Keywords: non-standard asymptotics; partially linear model; many terms; adjusted variance
Date issued
2016-01
URI
http://hdl.handle.net/1721.1/114435
Department
Massachusetts Institute of Technology. Department of Economics
Journal
Econometric Theory
Publisher
Cambridge University Press
Citation
Cattaneo, Matias D. et al. “ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS.” Econometric Theory (October 2016): 1–25 © 2016 Cambridge University Press
Version: Original manuscript
ISSN
0266-4666
1469-4360

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