'Creating a regression summary table with multiple regressions, adding 1 independent variable at a time (R/Python)
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!
Solution 1:[1]
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))
Solution 2:[2]
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
Sources
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Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | |
| Solution 2 | ranemak |