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Using the Helmert-Transformation to Reduce Dimensionality in a Mixed Model: Application to a Wage Equation with Worker and Firm Heterogeneity

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  • Nilsen, Øivind Anti

    (Norwegian School of Economics)

  • Raknerud, Arvid

    (Statistics Norway)

  • Skjerpen, Terje

    (Statistics Norway)

Abstract

A model for matched data with two types of unobserved heterogeneity is considered – one related to the observation unit, the other to units to which the observation units are matched. One or both of the unobserved components are assumed to be random. This mixed model allows identification of the effect of time-invariant variables on the observation units. Applying the Helmert transformation to reduce dimensionality simplifies the computational problem substantially. The framework has many potential applications; we apply it to wage modeling. Using Norwegian manufacturing data shows that the assumption with respect to the two types of heterogeneity affects the estimate of the return to education considerably.

Suggested Citation

  • Nilsen, Øivind Anti & Raknerud, Arvid & Skjerpen, Terje, 2011. "Using the Helmert-Transformation to Reduce Dimensionality in a Mixed Model: Application to a Wage Equation with Worker and Firm Heterogeneity," IZA Discussion Papers 5847, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp5847
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    More about this item

    Keywords

    matched employer-employee data; ECM-algorithm; high-dimensional two-way unobserved components;
    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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