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

Øivind Nilsen, Arvid Raknerud () and Terje Skjerpen
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Arvid Raknerud: Statistics Norway

No 5847, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: matched employer-employee data; ECM-algorithm; high-dimensional two-way unobserved components (search for similar items in EconPapers)
JEL-codes: C23 C81 J31 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2011-07
New Economics Papers: this item is included in nep-ecm and nep-lab
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Published - revised version published as 'Estimation of a model for matched panel data with high-dimensional two-way unobserved heterogeneity' in: Empirical Economics, 2017, 53, 1657–1680

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Related works:
Working Paper: Using the Helmert-transformation to reduce dimensionality in a mixed model: Application to a wage equation with worker and firm heterogeneity (2011)
Working Paper: Using the Helmert-transformation to reduce dimensionality in a mixed model: An application to a wage equation with worker and firm heterogeneity (2011)
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