As part of my master thesis, I'm performing several tests on panel data. One of these is a Fisher-type unit-root test, which works well with an unbalanced panel. I have performed the test, but I haven't managed to find an explanation of how to interpret the results.
This is the setup:
- Fisher-type test
- Time-trend included
- Cross-sectional mean removed
- Variables are being lagged once
The code that makes this happen is:
. xtunitroot fisher beta, dfuller trend demean lags(1)
The output for variable beta is:
Fisher-type unit-root test for beta Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Number of panels = 5 Ha: At least one panel is stationary Number of periods = 61 AR parameter: Panel-specific Asymptotics: T -> Infinity Panel means: Included Time trend: Included Cross-sectional means removed Drift term: Not included ADF regressions: 1 lag Statistic p-value Inverse chi-squared(10) P 77.8047 0.0000 Inverse normal Z -7.2246 0.0000 Inverse logit t(29) L* -9.7556 0.0000 Modified inv. chi-squared Pm 15.1616 0.0000 P statistic requires number of panels to be finite. Other statistics are suitable for finite or infinite number of panels.
- Based on the results, does my data contain a unit-root, or is it stationary?
- How do I know the confidence level at which I can accept/reject H0?