A Bayesian Approach to Account for Misclassification in Prevalence and Trend Estimation*

Martijn van Hasselt, Christopher Bollinger and Jeremy Bray

No 19-13, UNCG Economics Working Papers from University of North Carolina at Greensboro, Department of Economics

Abstract: In this paper we present a Bayesian approach to estimate the mean of a binary variable and changes in the mean over time, when the variable is subject to misclassification error. These parameters are partially identified and we derive identified sets under various assumptions about the misclassification rates. We apply our method to estimating the prevalence and trend of prescription opioid misuse, using data from the 2002-2014 National Survey on Drug Use and Health. Using a range of priors, the posterior distribution provides evidence that the prevalence of opioid misuse increases multiple times between 2002 and 2012.

Keywords: Misclassification; partial identification; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 I12 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2019-10-24
New Economics Papers: this item is included in nep-ecm, nep-hea and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:ris:uncgec:2019_013

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