So I have this project to work on with panel data. I am using a dataset featuring most European countries and some of my variables are Macroeconomic such as GDP, GDP per Capita. Thing is, for some cross-sections (countries), I have missing values. In one or two of them, there is like only one or two variables missing, whereas in 3 of them (eg. Latvia's GDP), I have like 5 missing values on the lower end. T goes up to 2018. If I fill the values, I should stand at about (N=19, T=29).
My first thought is to go forecast backwards with ARIMA and Smoothing in those with few observations missing (keep the one that doesn't give me silly negative GDP values) and maybe (linearly) extrapolate for those with more than two missing values?
Couple more questions - Bonus you could say; 1) Some countries, having been part of the USSR because independent in 1991. Should I start my dataset from 1991 simply to include them? Other choice is to include a year more. Still, some Macro varibles like CPI are actually available for those countries pre-1991. 2) How to check/test for heterogeneity?
Few words about the project: It's about causality between energy sources and macro variables. The plan is to go about it using Pesaran's CD for Cross-Dependency, run Unit Root tests (I'll include CIPS in there). See now, to run CIPS on Stata, the panel must be balanced. After that, check for Cointegration. Then, if I do get a long-run relationship (cointegration found), I'll go for FMOLS/DOLS and finally run VECM to check for causality.
Note: The data mentioned is taken from World Bank