Working Paper: NBER ID: w17442
Authors: 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.
Keywords: robust inference; misspecified models; conditional estimation
JEL Codes: C01
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Conditional Estimand (C51) | Smaller Variance (C29) |
| Smaller Variance (C29) | Better Power for Tests (C12) |
| Choice Between Conditional and Unconditional Estimand (C13) | Implications for Inference (C20) |
| Conditional Estimand (C51) | More Precise Estimates (C13) |