Hello, I would like to regress the independent variable GINI (Gini Index) on the dependent variable GDPpc (GDP per capita growth). For GDPpc to reach its stationary form, its first difference had to be taken. However, GINI was already in a stationary form.
I tried two different regressions. One with GDPpc and GINI being both in their first difference and one with only GDPpc being in its first-difference.
The first regression (d1_GDPpc, d1_GINI) yields a really high p-value, while the second regression (d1_GDPpc, GINI) yields a low enough p-value for the effects to be statistically significant.
My questions are the following:
Is it possible to regress variables in their first difference with variables in their level? Which regression model should I choose?
Although the right approach is using time series analysis (first-differenced data shows that you have time series data), you are allowed to adopt this approach as well. However, its interpretation is not as understandable as the common regression models.
I'll throw in my two cents and say that I don't think you want to use ols in this case. The reason is that you seem to have a time series
structure which is best handled in most cases as Dr. Jamshidi suggested by time series methods.I would suggest a visit with someone who has experience in such things at your university. Best wishes, David Booth
Hi Kostas, In think the second model results are more reliable, because differencing of GINI is not necessary.
Check https://www.reed.edu/economics/parker/312/tschapters/S13_Ch_4.pdf
Also check https://www.statalist.org/forums/forum/general-stata-discussion/general/1511491-first-difference-with-dummy-variable-set-interpretation-of-coefficients
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