Finite mixture models for linked survey and administrative data

Stephen Jenkins and Fernando Rios-Avila ()

German Stata Users' Group Meetings 2022 from Stata Users Group

Abstract: Researchers use finite mixture models to analyze linked survey and administrative data on labor earnings (or similar variables), taking account of various types of measurement error in each data source. Different combinations of error-ridden and error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. We introduce a set of Stata commands to fit a general class of finite mixture models to fit to linked survey-administrative data. We also provide postestimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid earnings variables that combine information from both data sources.

Date: 2022-06-10
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Persistent link: https://EconPapers.repec.org/RePEc:boc:dsug22:01

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