Perform back-test of transfer function model with 2 input variable. For a specified tfm2 model and a given forecast origin, the command iterated between estimation and 1-step ahead prediction starting at the forecast origin until the (T-1)th observation, where T is the sample size.
1 2 |
y |
Data vector of dependent variable |
x |
Data vector of the first input (or independent) variable |
x2 |
Data vector of the second input variable if any |
ct |
Data vector of a given deterministic variable such as time trend, if any |
wt |
Data vector of co-integrated series between input and output variables if any |
orderN |
Order (p,d,q) of the regular ARMA part of the disturbance component |
orderS |
Order (P,D,Q) of the seasonal ARMA part of the disturbance component |
sea |
Seasonalityt, default is 12 for monthly data |
order1 |
Order (r,s,b) of the transfer function model of the first input variable, where r and s are the degrees of denominator and numerator polynomials and b is the delay |
order2 |
Order (r2,s2,b2) of the transfer function model of the second input variable, where 2r and s2 are the degrees of denominator and numerator polynomials and b2 is the delay |
orig |
Forecast origin with default being T-1, where T is the sample size |
Perform out-of-sample 1-step ahead prediction to evaluate a fitted tfm2 model
ferror |
1-step ahead forecast errors, starting at the given forecast origin |
mse |
out-of-sample mean squared forecast errors |
rmse |
root mean squared forecast errors |
mae |
out-of-sample mean absolute forecast errors |
nobf |
The number of 1-step ahead forecast errors computed |
rAR |
Regulard AR coefficients |
Ruey S. Tsay
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control, 3rd edition, Prentice Hall, Englewood Cliffs, NJ.
tfm2
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