alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

Abstract

Provides a routine to concentrate out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm proposed by Stammann (2018) arXiv:1707.01815 and is restricted to glm’s that are based on maximum likelihood estimation and non-linear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides an analytical bias-correction for binary choice models (logit and probit) derived by Fernandez-Val and Weidner (2016) doi:10.1016/j.jeconom.2015.12.014.

Date
Jul 31, 2018 12:00 AM
Event
Software
Avatar
Amrei Stammann
Post-Doctoral Researcher in Economics