TY  - RPRT
        AU  - Jenkins, Stephen P.
AU  - Rios-Avila, Fernando
        TI  - Finite Mixture Models for Linked Survey and Administrative Data: Estimation and Post-estimation
        PY  - 2021/May/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 14404
        UR  - https://www.iza.org/publications/dp14404
        AB  - Researchers use finite mixture models to analyze linked survey and administrative data on labour earnings (or similar variables), taking account of various types of measurement error in each data source. Different combinations of error-ridden and/or 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 post-estimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid earnings variables that combine information from both data sources.
        KW  - measurement error
KW  - linked survey and administrative data
KW  - finite mixture models
        ER  -