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.
Btfm2(y,x,x2=NULL,wt=NULL,ct=NULL,orderN=c(1,0,0),orderS=c(0,0,0),sea=12,
order1=c(0,1,0),order2=c(0,-1,0),orig=(length(y)-1))Data vector of dependent variable
Data vector of the first input (or independent) variable
Data vector of the second input variable if any
Data vector of a given deterministic variable such as time trend, if any
Data vector of co-integrated series between input and output variables if any
Order (p,d,q) of the regular ARMA part of the disturbance component
Order (P,D,Q) of the seasonal ARMA part of the disturbance component
Seasonalityt, default is 12 for monthly data
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
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
Forecast origin with default being T-1, where T is the sample size
1-step ahead forecast errors, starting at the given forecast origin
out-of-sample mean squared forecast errors
root mean squared forecast errors
out-of-sample mean absolute forecast errors
The number of 1-step ahead forecast errors computed
Regular AR coefficients
Perform out-of-sample 1-step ahead prediction to evaluate a fitted tfm2 model
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