Fit GLM's with High-Dimensional k-Way Fixed Effects
alpaca: A package for fitting glm's with high-dimensional -way fixe...
Asymptotic bias correction after fitting binary choice models with a o...
Extract estimates of average partial effects
Extract estimates of structural parameters
Extract coefficient matrix for average partial effects
Extract coefficient matrix for structural parameters
Efficiently fit negative binomial glm's with high-dimensional -way ...
Efficiently fit glm's with high-dimensional -way fixed effects
Set feglm Control Parameters
Extract feglm fitted values
Compute average partial effects after fitting binary choice models wit...
Efficiently recover estimates of the fixed effects after fitting `fegl...
Predict method for feglm fits
Print APEs
Print feglm
Print summary.APEs
Print summary.feglm
Generate an artificial data set for some GLM's with two-way fixed effe...
Summarizing models of class APEs
Summarizing models of class feglm
Compute covariance matrix after estimating APEs
Compute covariance matrix after fitting feglm
Provides a routine to partial 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 described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. 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 analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.
Useful links