stepwise function

Stepwise estimation tools

Functions to perform stepwise estimations in fixest models.

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)