Estimation of a general transfer function model with two input variables. The model can handle one output and up-to 2 input variables. The time series noise can assume multiplicative seasonal ARMA models.
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: Seasonality, 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
Details
Perform estimation of a general transfer function model with two input variables
Returns
estimate: Coefficient estimates
sigma2: Residual variance sigma-square
residuals: Residual series
varcoef: Variance of the estimates
Nt: The disturbance series
rAR: Regular AR coefficients
rMA: Regular MA coefficients
sAR: Seasonal AR coefficients
sMA: Seasonal MA coefficients
omega: Numerator coefficients of the first transfer function
delta: Denominator coefficients of the first transfer function
omega2: Numerator coefficients of the 2nd transfer function
delta2: Denominator coefficients of the 2nd transfer function
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.