I have data for four years on the number of foreign subsidiaries of 120 firms, along with some variables that explain this phenomenon. I was able to obtain decent results using only one year of my data with a GLM regression, Poisson family. My dependent variable is a count variable going from 0 to a maximum of 25, therefore a Poisson regression seems OK (in fact quasipoisson was better because of slight overdispersion). I would like to use my full dataset to obtain even better results. I was wondering if someone could give me some information as to which model would be best for my data, and also which R programs I could potentially use.
My model is quite simple and looks like this:
subsidiaries <- glm(Y ~ X1 + X2 + X3 + SIC, family=poisson, data=mydata)
X1, X2, X3 all vary every year and represent data such foreign sales, assets, employees etc. SIC is a dummy variable that I use to control for firm industry.
I would simply like to use a longitudinal model instead of this simple, cross-sectional one. I would only change my current model to include time effects. Thanks!