Description Usage Arguments Details Value References Examples
Make a Band Spectrum Regression using the comun frequencies in cross-spectrum .
1 | 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/)
1 2 3 |
Loading required package: taRifx
$datos
Y X F res
1 12458 65.72689 12438.74 19.26350
2 12822 67.48491 12909.66 -87.65586
3 13345 69.97484 13576.63 -231.63133
4 14288 72.98793 14383.75 -95.74524
5 15309 76.26133 15260.59 48.41183
6 16207 80.29488 16341.05 -134.05185
7 17290 83.50754 17201.62 88.37559
8 17805 85.91239 17845.81 -40.80958
9 19037 88.65090 18579.37 457.62803
10 19915 91.45826 19331.38 583.62284
11 20867 94.86328 20243.48 623.52297
12 21543 98.82299 21304.16 238.83875
13 21935 102.54758 22301.86 -366.86407
14 22253 103.69194 22608.40 -355.40283
15 21757 99.98619 21615.75 141.25334
16 22409 100.00000 21619.45 789.55406
17 20636 99.38237 21454.00 -818.00190
18 20663 97.30654 20897.95 -234.95105
19 19952 96.10971 20577.36 -625.35719
$Fregresores
1 2
X1 1 88.15634053
X2 0 -5.68444051
X3 0 -9.44842574
X4 0 -2.21612456
X5 0 -2.62417102
X6 0 -0.79654010
X7 0 -2.39713050
X8 0 -1.53918705
X9 0 -1.43696347
X10 0 -1.18967332
X11 0 -0.69982435
X12 0 -0.92147295
X13 0 -0.82056751
X14 0 -1.14883279
X15 0 -0.66396550
X16 0 -1.26963280
X17 0 -0.21300734
X18 0 -1.09411248
X19 0 -0.01302282
$Tregresores
1 2
[1,] 0.2294157 15.07878
[2,] 0.2294157 15.48210
[3,] 0.2294157 16.05333
[4,] 0.2294157 16.74458
[5,] 0.2294157 17.49555
[6,] 0.2294157 18.42091
[7,] 0.2294157 19.15794
[8,] 0.2294157 19.70965
[9,] 0.2294157 20.33791
[10,] 0.2294157 20.98196
[11,] 0.2294157 21.76313
[12,] 0.2294157 22.67155
[13,] 0.2294157 23.52603
[14,] 0.2294157 23.78856
[15,] 0.2294157 22.93841
[16,] 0.2294157 22.94157
[17,] 0.2294157 22.79988
[18,] 0.2294157 22.32365
[19,] 0.2294157 22.04908
$Nregresores
[1] 2
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