// runs the adf test on the Xdata and Ydata
// returns true if the t statistic is less than the t critical value and false if otherwise
// sig is the significant level as a decimal
public bool adfuller(double sig)
{
// the test builds an OLS regression model on Y and lagged X values and then uses the t statistic
// from that test and compares it to the t critical values
Vector <double> Xdiff = Vector <double> .Build.DenseOfArray(math.linalg.ArrayManipulation.diff(Xdata.ToArray()));
Vector <double> Xlag = Vector <double> .Build.Dense(Xdata.Count - 1);
for (int i = 1; i < Xdata.Count; i++)
{
Xlag[i - 1] = Xdata[i];
}
Vector <double> Xlagdiff = Vector <double> .Build.DenseOfArray(math.linalg.ArrayManipulation.diff(Xlag.ToArray()));
Matrix <double> Xcomb = Matrix <double> .Build.Dense(Xlag.Count, 2);
Xcomb[0, 0] = Xlag[0];
Xcomb[0, 1] = 0;
for (int i = 0; i < Xlagdiff.Count; i++)
{
Xcomb[i + 1, 0] = Xlag[i + 1];
Xcomb[i + 1, 1] = Xlagdiff[i];
}
Vector <double> ytest = Vector <double> .Build.Dense(Xlag.Count);
Xdata.CopySubVectorTo(ytest, 2, 0, Xdata.Count - 2);
RegressionModel OLS_instance = new RegressionModel(Xcomb, ytest, "OLS");
Matrix <double> design = OLS_instance.designMatrix(Xcomb);
Vector <double> coeff = OLS_instance.fit(design, ytest);
double tstat = OLS_instance.testValue(Xcomb, Xdata, 1, coeff);
int abs50 = Math.Abs(Xdata.Count - 50);
int abs100 = Math.Abs(Xdata.Count - 100);
int abs200 = Math.Abs(Xdata.Count - 200);
int significance = (int)(sig * 100);
int min = Math.Min(abs50, Math.Min(abs100, abs200));
if (min == abs50)
{
if (min < TCritcalVals[50][significance])
{
return(true);
}
else
{
return(false);
}
}
else if (min == abs100)
{
if (min < TCritcalVals[100][significance])
{
return(true);
}
else
{
return(false);
}
}
else
{
if (min < TCritcalVals[200][significance])
{
return(true);
}
else
{
return(false);
}
}
}
