Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/98704
Authors: 
González de San Román, Ainara
Rebollo-Sanz, Yolanda F.
Year of Publication: 
2014
Series/Report no.: 
Economics Discussion Papers 2014-28
Abstract: 
In this paper, the authors develop a new estimation method that is suitable for censored models with two high-dimensional fixed effects and that is based on a sequence of least squares regressions, yielding significant savings in computing time and hence making it applicable to frameworks in which standard estimation techniques become unfeasible. The authors analyze its theoretical properties and evaluate its practical performance in small samples through a detailed Monte Carlo study. Finally, using a longitudinal match employer-employee dataset from Spain, they show that the biases encountered when ignoring censored issues can be significant to the role of firms in terms of wage dispersion: individual heterogeneity explains more than 60% of wage dispersion.
Subjects: 
fixed effects
algorithm
wage decomposition
censoring
simulation
assortative matching
JEL: 
I21
I24
J16
J31
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Working Paper

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