I have a small N large T panel which I am estimating via plm (panel linear regression model), with fixed effects.
Is there any way to get predicted values for a new dataset? (I want to estimate parameters on a subset of my sample, and then use these to calculate model-implied values for the whole sample).
Thanks!
There are (at least) two methods in the package to produce estimates from plm objects:
-- fixef.plm: Extract the Fixed Effects
-- pmodel.response: A function to extract the model.response
It appears to me that the author(s) are not interested in providing estimates for the "random effects". It may be a matter of "if you don't know how to do it on your own, then we don't want to give you a sharp knife to cut yourself too deeply."
I wrote a function called predict.out.plm that can create predictions for the original data and for a manipulated data set (with equal column names).
The predict.out.plm calculates a) the predicted (fitted) outcome of the transformed data and b) constructs the according to level outcome. The function works for First Difference (FD) estimations and Fixed Effects (FE) estimations using plm. For FD it creates the differenced outcome over time and for FE it creates the time-demeaned outcome.
The function is largely untested, and probably only works with strongly balanced data frames.
Any suggestions and corrections are very welcome. Help to develop a small R package would be very appreciated.
The function predict.out.plm
predict.out.plmTesting the the function:
##packages library(plm) ##test dataframe #data structure NLooks like there is a new package to do in-sample predictions for a variety of models including plm
https://cran.r-project.org/web/packages/prediction/prediction.pdf