A Stata program to combine robust regression estimation (Huber M) with two-way fixed effects. The program accompanies the Gassen & Veenman (2025) study on ``Estimation Precision and Robust Inference in Archival Research'' (https://ssrn.com/abstract=4975569).
Comments/feedback welcome
robtwfe is a program that can be used to combine Huber M-estimation with two-way fixed effects (FE). For a firm-time panel dataset, the program mimics robreg for Huber M-estimation with one FE dimension (firm) absorbed and the other FE dimension (time) included as indicator variables. Instead of including the time indicator variables, the program leverages (a) the functionality of reghdfe and (b) the fact that the iterative reweighting in the robust estimation relies on a sequence of weighted least squares estimations, which can be combined with two-way FE using reghdfe. When the second (time) dimension becomes sufficiently large (e.g., >50), the program is substantially faster than robreg and provides the same estimates.
The program can be installed by simply saving the *.ado file into your local Stata directory that contains additional ado programs. To identify this folder, type and execute "sysdir" in your Stata command window and go to the folder listed at "PLUS". Make sure to place the program in the folder with the correct starting letter (i.e., the folder named "r") and the file extension is correctly specified as *.ado.
The program requires moremata, reghdfe, hdfe, and robreg to be installed in Stata:
ssc inst moremata, replace
ssc inst reghdfe, replace
ssc inst hdfe, replace
ssc inst robreg, replace
Syntax:
robtwfe m depvar indepvars, ivar(string) tvar(string) cluster(string) eff(numeric) [options]
- depvar is the dependent variable;
- indepvars is/are the independent variable(s);
- ivar() refers to the variable indicating the unit fixed effect to be absorbed, similar to the option in
robreg; - tvar() refers to the variable indicating the time fixed effect to be absorbed;
- cluster() refers to the variable specifying the dimension at which standard errors should be clustered (nesting of FE not required);
- eff() refers to the normal efficiency of the robust estimation;
Optional program options in [options]:
- tol(numeric): specifies the tolerance for convergence of the iterative reweighted least squares (default 1e-10);
- weightvar(string): specifies the name of a new variable to be generated with robust regression weights;
- omitr2: do not compute and report the pseudo R2 (small speed benefit);
The following example shows the standard output from the program (the program can be tested using the test_robtwfe.do file):
