Finite Mixture Models for Linked Survey and Administrative Data: Estimation and Post-estimation

Stephen Jenkins and Fernando Rios-Avila ()

No 14404, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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.

Keywords: measurement error; linked survey and administrative data; finite mixture models (search for similar items in EconPapers)
JEL-codes: C81 C83 D31 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2021-05
New Economics Papers: this item is included in nep-ltv and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published - published in: Stata Journal, 2023, 23 (1), 53 - 85

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Journal Article: Finite mixture models for linked survey and administrative data: Estimation and postestimation (2023)
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