predict method for felm from lfe package

Question

Does anyone have a nice clean way to get predict behavior for felm models?

library(lfe) model1 <- lm(data = iris, Sepal.Length ~ Sepal.Width + Species) predict(model1, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Works  model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species) predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Does not work 

Answer 1

UPDATE (2020-04-02): The answer from Grant below using the new package fixest provides a more parsimonious solution.

As a workaround, you could combine felm, getfe, and demeanlist as follows:

library(lfe)  lm.model <- lm(data=demeanlist(iris[, 1:2], list(iris$Species)), Sepal.Length ~ Sepal.Width) fe <- getfe(felm(data = iris, Sepal.Length ~ Sepal.Width | Species)) predict(lm.model, newdata = data.frame(Sepal.Width = 3)) + fe$effect[fe$idx=="virginica"] 

The idea is that you use demeanlist to center the variables, then lm to estimate the coefficient on Sepal.Width using the centered variables, giving you an lm object over which you can run predict. Then run felm+getfe to get the conditional mean for the fixed effect, and add that to the output of predict.

Answer 2

Late to the party, but the new fixest package (link) has a predict method. It supports high-dimensional fixed effects (and clustering, etc.) using a very similar syntax to lfe. Somewhat remarkably, it is also considerably faster than lfe for the benchmark cases that I've tested.

library(fixest)  model_feols <- feols(data = iris, Sepal.Length ~ Sepal.Width | Species) predict(model_feols, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Works