Predicting with plm function in R

Question

I was wondering if it is possible to predict with the plm function from the plm package in R for a new dataset of predicting variables. I have create a model object using:

model <- plm(formula, data, index, model = 'pooling')

Now I'm hoping to predict a dependent variable from a new dataset which has not been used in the estimation of the model. I can do it through using the coefficients from the model object like this:

col_idx <- c(...)
df <- cbind(rep(1, nrow(df)), df[(1:ncol(df))[-col_idx]])
fitted_values <- as.matrix(df) %*% as.matrix(model_object$coefficients)

Such that I first define index columns used in the model and dropped columns due to collinearity in col_idx and subsequently construct a matrix of data which needs to be multiplied by the coefficients from the model. However, I can see errors occuring much easier with the manual dropping of columns.

A function designed to do this would make the code a lot more readable I guess. I have also found the pmodel.response() function but I can only get this to work for the dataset which has been used in predicting the actual model object.

Any help would be appreciated!

Answer 1

I wrote a function (predict.out.plm) to do out of sample predictions after estimating First Differences or Fixed Effects models with plm.

The function is posted here:

https://stackoverflow.com/a/44185441/2409896