NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Robust Inference for Misspecified Models Conditional on Covariates

Alberto Abadie, Guido W. Imbens, Fanyin Zheng

NBER Working Paper No. 17442
Issued in September 2011
NBER Program(s):   TWP

Following the work by White (1980ab; 1982) it is common in empirical work in economics to report standard errors that are robust against general misspecification. In a regression setting these standard errors are valid for the parameter that in the population minimizes the squared difference between the conditional expectation and the linear approximation, averaged over the population distribution of the covariates. In nonlinear settings a similar interpretation applies. In this note we discuss an alternative parameter that corresponds to the approximation to the conditional expectation based on minimization of the squared difference averaged over the sample, rather than the population, distribution of a subset of the variables. We argue that in some cases this may be a more interesting parameter. We derive the asymptotic variance for this parameter, generally smaller than the White robust variance, and we propose a consistent estimator for the asymptotic variance.

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Document Object Identifier (DOI): 10.3386/w17442

Published: Alberto Abadie, Guido W. Imbens & Fanyin Zheng pages 1601-1614 Inference for Misspecified Models With Fixed Regressors Journal of the American Statistical Association Volume 109, Issue 508, 2014

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