Finite mixture models for linked survey and administrative data: Estimation and postestimation

Stephen Jenkins and Fernando Rios-Avila ()

Stata Journal, 2023, vol. 23, issue 1, 53-85

Abstract: Researchers use finite mixture models to analyze linked survey and administrative data on labor earnings, while also accounting for 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 suite of commands to fit finite mixture models to linked survey-administrative data: there is a general model and seven simpler variants. We also provide postestimation commands for assessment of reliability, marginal effects, data simulation, and pre- diction of hybrid variables that combine information from both data sources about the outcome of interest. Our commands can also be used to study measurement errors in other variables besides labor earnings.

Keywords: ky_fit; ky_estat; ky_sim; linked survey and administrative data; measurement error; finite mixture models; latent class models (search for similar items in EconPapers)
Date: 2023
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http://hdl.handle.net/10.1177/1536867X231161976

Related works:
Working Paper: Finite Mixture Models for Linked Survey and Administrative Data: Estimation and Post-estimation (2021)
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DOI: 10.1177/1536867X231161976

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