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6
The KPSS test is used for testing a null hypothesis that an observable time series is stationary around a deterministic trend. You can see that the critical values are given by:
Critical values for H0: mIlliq1 is trend stationary
So as you can see, you cannot reject the H0, that your data is trend-stationary. So the data follow a straight-line time trend ...
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You are likely to be over-emphasising here significance as a yes-no matter. P = 0.05, or whatever, does not demarcate valid choices from invalid choices. Also, "multicollinearity" is too strong a word just for relationships among predictors.
There is no infallible solution that can be suggested remotely without seeing your data and learning more about your ...
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As a general advice, your sample size is quite small. It's not such a no-no as some people claim but depending on the specifics of the data, it's not too surprising to have unstable or unexpected results in a factor analysis.
A big question in all this is how you selected the number of factors to extract. There is no objective easy-to-determine “number of ...
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You calculate the full transformed confidence interval and then transform it back.
Let's say the transformed confidence interval is 5 ± 3, or CI95% = [2, 8]. You would take the 2, and 8 values and transform them back. You do NOT transform the 3 (the width of the confidence interval). The result in this example CI95% = [0.25, 0.016].
Be careful of ...
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You need to study (or search for) "power calculations" which allows you to determine your sample size and your test power. You can calculate power for your running test from your sample size and some other factors such as variance and the type of the test used and sometimes other factors. A power > 0.8 is usually considered as appropriate.
If your test ...
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This can usually be done with the margins command, but gologit2 is an older user-written command that does not take factor variables, so the marginal effects will be off by treating the dummies as if they were continuous.
I would estimate your model, use margeff, preserve the data, set the dummies to the base level, set all the continuous variables to zero ...
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