Hi!
In my panel dataset, I find evidence of both non-stationarity and cross-sectional dependence when running the Pesaran CIPS unit root test and the Pesaran CD test on the full sample. One independent variable, carbon_tax, has a value of zero for many observations. When I drop all observations where carbon_tax = 0, my sample size falls from about 1,100 to 500 observations. In this reduced sample, both issues disappear: the variables appear stationary and there is no cross-sectional dependence. My question is whether it is econometrically sound to remove these zero-value observations (and thus half of my sample), and what this means for the validity and interpretation of my regression results.
Thanks in advance!
In my panel dataset, I find evidence of both non-stationarity and cross-sectional dependence when running the Pesaran CIPS unit root test and the Pesaran CD test on the full sample. One independent variable, carbon_tax, has a value of zero for many observations. When I drop all observations where carbon_tax = 0, my sample size falls from about 1,100 to 500 observations. In this reduced sample, both issues disappear: the variables appear stationary and there is no cross-sectional dependence. My question is whether it is econometrically sound to remove these zero-value observations (and thus half of my sample), and what this means for the validity and interpretation of my regression results.
Thanks in advance!
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