Creating a regression summary table with multiple regressions, adding 1 independent variable at a time (R/Python)

Question

I would like to know, whether there is a pre-built function / package which does a simply OLS regression, by adding one independent variable from a pre-defined set to see, how to coefficients and their significance evolves by adding those variables.

Doing the regressions with a for-loop wouldn't be a problem, but I just wonder whether there is some function for displaying the summary as such, as I see this format very often in academic finance papers.

Attached picture: you can see regression (1) is just a univariate regression with "Mkt-Rf" as independent variable. In regression (2), we add "SMB" and "HML" variables.

Either R or Python package which does this would be great, ideally both. Thank you!

user_14137004 · Answer

Have a look at the fixest package, there you have the option of stepwise estimation tools https://lrberge.github.io/fixest/reference/stepwise.html

Example:

base = iris
names(base) = c("y", "x1", "x2", "x3", "species")
library(fixest)
etable(feols(y ~ sw(x1, x2, x3), base))

user_18975804 · Answer

There is the stargazer package in which creates a table from the regressions. You can add multiple regressions and it creates the table. Stargazer Examples

Creating a regression summary table with multiple regressions, adding 1 independent variable at a time (R/Python)

2 Answers