Department of Economics and Business Economics

Alternative asymptotics and the partially linear model with many regressors

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  • Matias D. Cattaneo, University of Michigan, United States
  • Michael Jansson
  • Whitney K. Newey, Massachusetts Institute of Technology, United States
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.
Original languageEnglish
JournalEconometric Theory
Volume34
Issue2
Pages (from-to)277-301
Number of pages25
ISSN0266-4666
DOIs
Publication statusPublished - 2018

    Research areas

  • DENSITY, DISTRIBUTIONS, ESTIMATORS, INSTRUMENTAL VARIABLES, JACKKNIFE, NONPARAMETRIC REGRESSION, QUADRATIC-FORMS, ROBUST REGRESSION, SERIES ESTIMATION, WEIGHTED AVERAGE DERIVATIVES

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