internal static Matrix CovarianceMatrixSerialCorrelationCorrected(double[] array1, double[] array2, double decayFactor, int length)
{
double[] array1Len = Tools.MostRecentValues(array1, length);
double[] array2Len = Tools.MostRecentValues(array2, length);
double[] array1Lag = Tools.LagArray(array1, length, 1);
double[] array2Lag = Tools.LagArray(array2, length, 1);
double[,] lagArray = new double[length, 2];
for (int i = 0; i < length; i++)
{
lagArray[i, 0] = array1Lag[i];
lagArray[i, 1] = array2Lag[i];
}
WeightedLeastSquares wls1 = new WeightedLeastSquares(array1Len, lagArray, true, decayFactor);
WeightedLeastSquares wls2 = new WeightedLeastSquares(array2Len, lagArray, true, decayFactor);
Matrix D = new Matrix(2, 2);
D[0, 0] = wls1.Betas[1].Value;
D[0, 1] = wls1.Betas[2].Value;
D[1, 0] = wls2.Betas[1].Value;
D[1, 1] = wls2.Betas[2].Value;
Matrix C = new Matrix(2, 2);
C[0, 0] = Variance(array1Len, decayFactor);
C[0, 1] = Covariance(array1Len, array2Len, decayFactor);
C[1, 0] = C[0, 1];
C[1, 1] = Variance(array2Len, decayFactor);
Matrix M = SerialCorrelationCorrection.SerialCorrelationVarianceCorrection(D, C);
Matrix CStar = M.Multiply(C);
return(CStar);
}
internal static Matrix CovarianceMatrixSerialCorrelationCorrected(double[] array1, double[] array2, double decayFactor, int length)
{
double[] array1Len = Tools.MostRecentValues(array1, length);
double[] array2Len = Tools.MostRecentValues(array2, length);
double[] array1Lag = Tools.LagArray(array1, length, 1);
double[] array2Lag = Tools.LagArray(array2, length, 1);
double[,] lagArray = new double[length, 2];
for (int i = 0; i < length; i++)
{
lagArray[i, 0] = array1Lag[i];
lagArray[i, 1] = array2Lag[i];
}
WeightedLeastSquares wls1 = new WeightedLeastSquares(array1Len, lagArray, true, decayFactor);
WeightedLeastSquares wls2 = new WeightedLeastSquares(array2Len, lagArray, true, decayFactor);
Matrix D = new Matrix(2, 2);
D[0, 0] = wls1.Betas[1].Value;
D[0, 1] = wls1.Betas[2].Value;
D[1, 0] = wls2.Betas[1].Value;
D[1, 1] = wls2.Betas[2].Value;
Matrix C = new Matrix(2, 2);
C[0, 0] = Variance(array1Len, decayFactor);
C[0, 1] = Covariance(array1Len, array2Len, decayFactor);
C[1, 0] = C[0, 1];
C[1, 1] = Variance(array2Len, decayFactor);
Matrix M = SerialCorrelationCorrection.SerialCorrelationVarianceCorrection(D, C);
Matrix CStar = M.Multiply(C);
return CStar;
}
