rdf {descomponer} | R Documentation |
Make a Band Spectrum Regression using the comun frequencies in cross-spectrum .
rdf(y,x)
y |
a Vector of the dependent variable |
x |
a Vector of the independent variable |
Transforms the time series in amplitude-frequency domain, order the fourier coefficient by the comun frequencies in cross-spectrum, make a band spectrum regresion (Parra, F. ,2013) of the serie y_t and x_t for every set of fourier coefficients, and select the model to pass the Durbin test in the significance chosen.
If not find significance for Band Spectrum Regression, make a OLS.
The generalized cross validation (gcv), is caluculated by: gcv=n*sse/((n-k)^2)
where "sse" is the residual sums of squares, "n" the observation, and k the coefficients used in the band spectrum regression.
Slow computer in time series higher 1000 data.
The output is a data.frame object.
datos$Y |
The Y time-serie |
datos$X |
The X time-serie |
datos$F |
The time - serie fitted |
datos$reg |
The error time-serie |
Fregresores |
The matrix of regressors choosen in frequency domain |
Tregresores |
The matrix of regressors choosen in time domain |
Nregresores |
The coefficient number of fourier chosen |
sse |
Residual sums of squares |
gcv |
Generalized Cross Validation |
DURBIN, J., "Tests for Serial Correlation in Regression Analysis based on the Periodogram ofLeast-Squares Residuals," Biometrika, 56, (No. 1, 1969), 1-15.
Engle, Robert F. (1974), Band Spectrum Regression,International Economic Review 15,1-11.
Harvey, A.C. (1978), Linear Regression in the Frequency Domain, International Economic Review, 19, 507-512.
Parra, F. (2014), Amplitude time-frequency regression, (http://econometria.wordpress.com/2013/08/21/estimation-of-time-varying-regression-coefficients/)
data(PIB)
data(celec)
rdf(celec,PIB)