Estimation of a model for matched panel data with high-dimensional two-way unobserved heterogeneity

We consider a model for matched data with two types of unobserved effects: a random effect related to the main observational unit and a random or fixed effect related to a secondary unit to which the main unit is matched. In typical applications, e.g., on registry data, there is a curse of dimension...

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Bibliographic Details
Published inEmpirical economics Vol. 53; no. 4; pp. 1657 - 1680
Main Authors Nilsen, Øivind A., Raknerud, Arvid, Skjerpen, Terje
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017
Springer Nature B.V
Subjects
Econometrics
Economic models
Economic theory
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Educational attainment
Endogenous
Finance
Insurance
Longitudinal studies
Management
Panel data
Random effects
Regression analysis
Statistics for Business
Transformation
Wages & salaries
Helmert transformation
C23
Random effects
Wage equation
J31
Iterative feasible GLS
C81
Matched employer–employee data
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Summary:We consider a model for matched data with two types of unobserved effects: a random effect related to the main observational unit and a random or fixed effect related to a secondary unit to which the main unit is matched. In typical applications, e.g., on registry data, there is a curse of dimensionality which we propose to mitigate using an iterative feasible GLS approach on variables subjected to the Helmert transformation. Control functions allow for correlation between the explanatory variables and the random effects. This approach is illustrated by a wage equation with unobserved individual- and firm-specific effects and an endogenous years-of-schooling variable.
ISSN:0377-7332
1435-8921
DOI:10.1007/s00181-016-1179-0