Estimating high-dimensional fixed-effects models
In this presentation, I describe an alternative iterative approach for the estimation of linear regression models with high-dimensional fixed-effects, such as large employer–employee datasets. This approach is computationally intensive but imposes minimum memory requirements. I also show that the approach can be extended to nonlinear models and potentially to more than two high-dimensional fixed effects. Note: The presentation is based on a paper that is currently under review at the Stata Journal.
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