Dear All,
I have a few queries with regard to my dataset which is unbalanced and contains data for T=54 years andN=41 countries.
My Pooled OLS and FE regressions are:
reg ln(suicide) l.WBrelativegdp l.WBrelativegdp2 year wbgdp wbunemployment hexp divorce alc, cluster(countrynum)
xtreg ln(suicide) l.WBrelativegdp l.WBrelativegdp2 year wbgdp wbunemployment hexp divorce alc, cluster(countrynum) fe
I have undertaken a fisher unit root test for panel data and have found that all variables are non stationary.
My questions are as follows:
Thank you for your guidance,
I have a few queries with regard to my dataset which is unbalanced and contains data for T=54 years andN=41 countries.
My Pooled OLS and FE regressions are:
reg ln(suicide) l.WBrelativegdp l.WBrelativegdp2 year wbgdp wbunemployment hexp divorce alc, cluster(countrynum)
xtreg ln(suicide) l.WBrelativegdp l.WBrelativegdp2 year wbgdp wbunemployment hexp divorce alc, cluster(countrynum) fe
I have undertaken a fisher unit root test for panel data and have found that all variables are non stationary.
My questions are as follows:
- What will the effect be of non-stationary variables included? Will my coefficients be biased or is it that I am simply worried about spurious relationships?
- Is T=54 too small for me to worry about non-stationarity/ Has my ADF falsely found non-stationarity as a result of this small T? The reason I worry about this is because looking at the panel graphs for my main variables [ln(suicide) and l.RelativeGDP], most panels look stationary. Are my results driven by certain countries? Should I believe my ADF results?
- However, when I take the first differences of these variables I do agree that they become more stationary so perhaps taking the first differences is the solution to this issue?
- Can Fixed effects overcome the issue of non-stationarity? If so, how?
Thank you for your guidance,
