| stepwise {fixest} | R Documentation |
Stepwise estimation tools
Description
Functions to perform stepwise estimations in fixest models.
Usage
sw(...)
csw(...)
sw0(...)
csw0(...)
mvsw(...)
Arguments
... |
Represents formula variables to be added in a stepwise fashion to an estimation. |
Details
To include multiple independent variables, you need to use the stepwise functions.
There are 5 stepwise functions: sw, sw0, csw, csw0 and mvsw. Let's explain that.
Assume you have the following formula: fml = y ~ x1 + sw(x2, x3). The stepwise
function sw will estimate the following two models: y ~ x1 + x2 and y ~ x1 + x3.
That is, each element in sw() is sequentially, and separately, added to the formula.
Would have you used sw0 in lieu of sw, then the model y ~ x1 would also have
been estimated. The 0 in the name implies that the model without any stepwise
element will also be estimated.
Finally, the prefix c means cumulative: each stepwise element is added to the next.
That is, fml = y ~ x1 + csw(x2, x3) would lead to the following models y ~ x1 + x2
and y ~ x1 + x2 + x3. The 0 has the same meaning and would also lead to the model
without the stepwise elements to be estimated: in other words,
fml = y ~ x1 + csw0(x2, x3) leads to the following three models: y ~ x1,
y ~ x1 + x2 and y ~ x1 + x2 + x3.
The last stepwise function, mvsw, refers to 'multiverse' stepwise. It will estimate
as many models as there are unique combinations of stepwise variables. For example
fml = y ~ x1 + mvsw(x2, x3) will estimate y ~ x1, y ~ x1 + x2, y ~ x1 + x3,
y ~ x1 + x2 + x3. Beware that the number of estimations grows pretty fast (2^n,
with n the number of stewise variables)!
Examples
base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
# Regular stepwise
feols(y ~ sw(x1, x2, x3), base)
# Cumulative stepwise
feols(y ~ csw(x1, x2, x3), base)
# Using the 0
feols(y ~ x1 + x2 + sw0(x3), base)
# Multiverse stepwise
feols(y ~ x1 + mvsw(x2, x3), base)