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...

In many applications of conditional logit models the choice set and the characteristics of that set are identical for groups of decision makers. In that case is possible to obtain a more computationally efficient estimation of the model by grouping the data and employing a new user-written...

In this presentation, I provide a detailed discussion of an alternative iterative approach for the estimation of linear regression models with two high-dimensional fixed-effects, such as large employer-employee datasets. I also show how to extend the approach to three high-dimensional fixed...