Hi all,
I am investigating the impact of the degree of competitiveness of English football leagues on fan attendance within the league using a panel data set and have estimated the following model:
xtreg lnatt scr pts goals lag_pts ticket rgnu lncap lnpop, re
where att is attendance of each club for each season, scr is my measure of competitiveness, pts is point acquired by each club in the league, goals is goals scored by each club in the league, lag_pts is points acquired in the previous season, ticket is the minimum ticket price in that season, rgnu is the regional unemployment rate, cap is stadium capacity and pop is population.
Firstly, I wanted to know if I am justified in using a random effects model due to the fact that stadium capacity is largely time invariant and so would be omitted from the regression in a fixed effects model or should I perform a hausman test (and if so is using a 95% confidence interval correct for this test)?
Additionally, I have 355 observations for all my variables aside from ticket prices for which I only have 155 observations. When I estimate the model excluding ticket prices the outcome is very different and my variable of interest (scr) goes from a p-value of 0.1 to around 0.9. Is this as concerning as it seems? Should I still include ticket prices in my estimated model?
Any help would be hugely appreciated.
Thanks in advance,
Joe
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