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

Author

Listed:
  • Øivind A. Nilsen

    (Norwegian School of Economics)

  • Arvid Raknerud

    (Statistics Norway, Research Department)

  • Terje Skjerpen

    (Statistics Norway, Research Department)

Abstract

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.

Suggested Citation

  • Øivind A. Nilsen & Arvid Raknerud & Terje Skjerpen, 2017. "Estimation of a model for matched panel data with high-dimensional two-way unobserved heterogeneity," Empirical Economics, Springer, vol. 53(4), pages 1657-1680, December.
    • Handle: RePEc:spr:empeco:v:53:y:2017:i:4:d:10.1007_s00181-016-1179-0
    DOI: 10.1007/s00181-016-1179-0
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    More about this item

    Keywords

    Matched employer–employee data; Helmert transformation; Random effects; Wage equation; Iterative feasible GLS;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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