I am currently writing a thesis and struggling with this problem: I have election data for N countries over a time period of 25 years. The elections were (obviously) held in different years. I need to conduct an analysis with country fixed effects and my supervisor told me I should use a method for panel data.
However, the data in my opinion is not a panel dataset, because of the different ts. Can someone give me any input which type of model I should use? Can I just pretend it is panel data?
I looked it up in different standard course books and the definition and examples are always datasets with N observations and T time points, whereas every combination was present.
However in my case I have:
country | year |
---|---|
AUT | 1990 |
AUT | 1995 |
AUT | 2000 |
BEL | 1991 |
BEL | 1994 |
BEL | 1998 |
... | ... |
The observations are all in the same time period but since an election is held every XX years the time points vary from country to country.
I then thought that it would be an unbalanced panel data set, however a dataset is labelled "unbalanced" mostly when some observations are missing and in this case I have most observations missing (since for every i there are only 5-8 t out of the twenty-five t).
For every election I used the share of populist voting as the dependent variable and regressed it on different economic indicators and on a dummy variable whether the election took place 5 years after a financial crises event or not.
x <- lm(share_populist ~ postcrisis_event + GDP + CPI +
factor(country_name_short), data_final)
summary(x)
.
I got a really high coefficient for the dummy variable and found that the other indicators are not significant. Comparing that to prior research in this field it does not make much sense.
Hence this is where my doubts came from.