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 among middle-aged White men, the prevalence of opioid misuse increased multiple times between 2002 and 2012.
|Number of pages
|Journal of Applied Econometrics
|Published - Mar 2022
Bibliographical noteFunding Information:
We thank John Pepper and Gary Koop for helpful comments on an earlier version of this paper.
© 2021 John Wiley & Sons, Ltd.
- Bayesian estimation
- partial identication
ASJC Scopus subject areas
- Social Sciences (miscellaneous)
- Economics and Econometrics