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Semiparametric quasi-likelihood estimation with missing data

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  • Francesco Bravo
  • David T. Jacho-Chávez

Abstract

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.

Suggested Citation

  • Francesco Bravo & David T. Jacho-Chávez, 2016. "Semiparametric quasi-likelihood estimation with missing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(5), pages 1345-1369, March.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:5:p:1345-1369
    DOI: 10.1080/03610926.2013.863928
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    Cited by:

    1. Francesco Bravo, 2020. "Robust estimation and inference for general varying coefficient models with missing observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 966-988, December.

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