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Robust Inference for Misspecified Models Conditional on Covariates

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Listed:
  • Alberto Abadie
  • Guido W. Imbens
  • Fanyin Zheng

Abstract

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.

Suggested Citation

  • Alberto Abadie & Guido W. Imbens & Fanyin Zheng, 2011. "Robust Inference for Misspecified Models Conditional on Covariates," NBER Working Papers 17442, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17442 Note: TWP
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    References listed on IDEAS

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    1. Dean Karlan & John A. List, 2007. "Does Price Matter in Charitable Giving? Evidence from a Large-Scale Natural Field Experiment," American Economic Review, American Economic Association, vol. 97(5), pages 1774-1793, December.
    2. Cameron,A. Colin & Trivedi,Pravin K., 2008. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9787111235767, December.
    3. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    4. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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