| predictFFF {descomponer} | R Documentation |
Prediction whit Regression in FFF
Description
Make a prediction for a rdf object
Usage
predictFFF(y,x,new)Arguments
y |
a Vector of the dependent variable |
x |
a Vector of the independent variable |
new |
A data frame in which to look for variables with which to predict. If omitted, the fitted values are used. |
Details
Use predict.lm, with interval="confidence"
Value
fit |
vector or matrix as above |
References
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.
Gallant; A. R.(1984), The Fourier Flexible Form. Amer. J. Agr. Econ.66(1984):204-15.
Parra, F. (2014), Amplitude time-frequency regression, (http://econometria.wordpress.com/2013/08/21/estimation-of-time-varying-regression-coefficients/)
Parra, F.(2021), Econometria con Series de Fourier (https://econometria.files.wordpress.com/2020/12/curso-de-econometria-avanzado.pdf)
Examples
data("ipi")
t=seq(1:length(ipi))
Mod1=FFF(ipi,t)
plot(ipi)
lines(Mod1$fitted)
new=(length(t)+1):(length(t)+12)
Mod2=predictFFF(ipi,t,new)