Overall Rates and Sample Selection: Inferring HIV Prevalence from a Selected Sample

30 Pages Posted: 24 May 2018

See all articles by Jessica Chan

Jessica Chan

United Nations - Department of Economic and Social Affairs (DESA)

Jonathan A Cook

U.S. Securities and Exchange Commission; affiliation not provided to SSRN

Date Written: May 1, 2018

Abstract

This paper estimates HIV prevalence in Zambia from survey data that are subject to sample selection: some surveyed individuals do not consent to taking a HIV test. We introduce semiparametric estimators for an overall rate that incorporate recent developments in machine learning. The semiparametric estimators perform well in Monte Carlo experiments and obtain narrower confidence intervals than a fully parametric estimator when the model is misspecified. Our semiparametric estimates of the HIV rate are roughly equal to the rate in the selected sample. In contrast, recent parametric estimates find a higher rate--implying that some form of sample-selection correction is warranted.

Keywords: HIV, Sample-Selection Bias, Overall Rates

JEL Classification: C34, C14, I19

Suggested Citation

Chan, Jessica and Cook, Jonathan A, Overall Rates and Sample Selection: Inferring HIV Prevalence from a Selected Sample (May 1, 2018). Available at SSRN: https://ssrn.com/abstract=3178231 or http://dx.doi.org/10.2139/ssrn.3178231

Jessica Chan

United Nations - Department of Economic and Social Affairs (DESA) ( email )

New York, NY 10017
United States

Jonathan A Cook (Contact Author)

U.S. Securities and Exchange Commission ( email )

affiliation not provided to SSRN

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