Estimates linear models with multiple group fixed effects

Simen Gaure, The Ragnar Frisch Centre for Economic Research

Two-Way Fixed Effects, Panel Data, Multiple Fixed Effects

Estimate linear models with multiple group fixed effects
Fixed Effect Estimator, Two-Way Fixed Effects, Panel Data,
Multiple Fixed Effects, Kaczmarz Method, Alternating
Projections

Transforms away factors with many levels prior to doing an
OLS.  Useful for estimating linear models with multiple group
fixed effects, and for estimating linear models which uses
factors as pure control variables.

Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors as pure control variables.

compiler, igraph, knitr, Rcgmin, stargazer
Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors as pure control variables.
Includes support for instrumented variables, robust and multi-way clustered standard errors.

Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors as pure control variables.
Includes support for instrumented variables, robust and multi-way clustered standard errors,
as well as limited mobility bias correction.

Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors as pure control variables.
Includes support for instrumental variables, conditional F statistics for weak instruments,
robust and multi-way clustered standard errors, as well as limited mobility bias correction.

Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors with many levels as pure control variables.
Includes support for instrumental variables, conditional F statistics for weak instruments,
robust and multi-way clustered standard errors, as well as limited mobility bias correction.