A simple feasible procedure to fit models with high-dimensional fixed effects
Paulo Guimarães
University of South Carolina
Columbia, South Carolina
and Universidade do Porto
Porto, Portugal
[email protected]
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Pedro Portugal
Banco de Portugal and
Universidade Nova de Lisboa
Lisbon, Portugal
[email protected]
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Abstract. In this article, we describe an iterative approach for the estimation
of linear regression models with high-dimensional fixed effects. This approach is
computationally intensive but imposes minimum memory requirements. We also
show that the approach can be extended to nonlinear models and to more than
two high-dimensional fixed effects.
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Paulo Guimarães, Pedro Portugal
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fixed effects, panel data
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