Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130060
Authors: 
Ichimura, Hidehiko
Newey, Whitney K.
Year of Publication: 
2015
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP44/15
Abstract: 
Often semiparametric estimators are asymptotically equivalent to a sample average. The object being averaged is referred to as the influence function. The influence function is useful in formulating primitive regularity conditions for asymptotic normality, in efficiency comparions, for bias reduction, and for analyzing robustness. We show that the influence function of a semiparametric estimator can be calculated as the limit of the Gateaux derivative of a parameter with respect to a smooth deviation as the deviation approaches a point mass. We also consider high level and primitive regularity conditions for validity of the influence function calculation. The conditions involve Frechet differentiability, nonparametric convergence rates, stochastic equicontinuity, and small bias conditions. We apply these results to examples.
Subjects: 
Influence function
semiparametric estimation
bias correction
JEL: 
C14
C24
H31
H34
J22
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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