Robust Inference for Misspecified Models Conditional on Covariates
Harvard University - Harvard Kennedy School (HKS); National Bureau of Economic Research (NBER)
Guido W. Imbens
University of California, Berkeley - Department of Economics; National Bureau of Economic Research (NBER); Institute for the Study of Labor (IZA)
affiliation not provided to SSRN
NBER Working Paper No. w17442
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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Number of Pages in PDF File: 25working papers series
Date posted: September 23, 2011
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