I'm trying to estimate the public spending role over economic growth. So basically i'm using an inverse Wagner Law to see how those two variables are correlated.
I have checked either GDP (PIB) and Public Spending, and both of them have unit roots. Integrated in order 2.
This is consistent with higher R^2 value and probably a spurious regression that shows ahead.
Code:
reg PIB_nominal_bm total_gasto_nom Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(1, 26) = 11070.25 Model | 2.0580e+30 1 2.0580e+30 Prob > F = 0.0000 Residual | 4.8334e+27 26 1.8590e+26 R-squared = 0.9977 -------------+---------------------------------- Adj R-squared = 0.9976 Total | 2.0628e+30 27 7.6400e+28 Root MSE = 1.4e+13 --------------------------------------------------------------------------------- PIB_nominal_bm | Coef. Std. Err. t P>|t| [95% Conf. Interval] ----------------+---------------------------------------------------------------- total_gasto_nom | 5.04e+09 4.79e+07 105.22 0.000 4.94e+09 5.14e+09 _cons | 3.52e+13 4.05e+12 8.68 0.000 2.69e+13 4.35e+13 ---------------------------------------------------------------------------------
Code:
predict u, res dfuller u Dickey-Fuller test for unit root Number of obs = 27 ---------- Interpolated Dickey-Fuller --------- Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value ------------------------------------------------------------------------------ Z(t) -3.732 -3.736 -2.994 -2.628 ------------------------------------------------------------------------------ MacKinnon approximate p-value for Z(t) = 0.0037
So when i ran the stationary test of the residuals of that regression. dfuller test says it's stationary. So i'm a bit confused, does this mean that relation is not spurious and it's telling me the long-term relationships?.
0 Response to Stationary residuals. Spurious regression?
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