Back-Test of a Transfer Function Model with Two Input Variables
Back-Test of a Transfer Function Model with Two Input Variables
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.
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
Details
Perform out-of-sample 1-step ahead prediction to evaluate a fitted tfm2 model
Returns
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: Regular AR coefficients
References
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.