Semiparametric quasi-likelihood estimation with missing data

This article develops quasi-likelihood estimation for generalized varying coefficient partially linear models when the response is not always observable. This article considers two estimation methods and shows that under the assumption of selection on the observables the resulting estimators are asy...

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Published in:Communications in statistics. Theory and methods 2016-03, Vol.45 (5), p.1345-1369
Main Authors: Bravo, Francesco, Jacho-Chávez, David T.
Format: Article
Language:English
Subjects:
Asymptotic properties
Backfitting
Computer simulation
Double robustness
Econometrics
Economic models
Estimators
Inverse probability weighting
Mathematical analysis
Mathematical models
Primary: 62G08
Profiling
Samples
Secondary: 62G20
Statistical analysis
Statistics
Unconfoundness
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description This article develops quasi-likelihood estimation for generalized varying coefficient partially linear models when the response is not always observable. This article considers two estimation methods and shows that under the assumption of selection on the observables the resulting estimators are asymptotically normal. As an application of these results this article proposes a new estimator for the average treatment effect parameter. A simulation study illustrates the finite sample properties of the proposed estimators.
doi_str_mv 10.1080/03610926.2013.863928
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subjects Asymptotic properties
Backfitting
Computer simulation
Double robustness
Econometrics
Economic models
Estimators
Inverse probability weighting
Mathematical analysis
Mathematical models
Primary: 62G08
Profiling
Samples
Secondary: 62G20
Statistical analysis
Statistics
Unconfoundness
title Semiparametric quasi-likelihood estimation with missing data
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