seasest(timeslab)R Documentation

Calculate Box-Jenkins Estimates for a Seasonal ARIMA Model

Description

Calculate Box-Jenkins Estimates for a Seasonal ARIMA Model

Usage

seasest(y,ords,coeffs,lags,back,maxit=50,eps=0.000001)

Arguments

y Array of length $n$ containing the data.
ords An array of length 5 containing the full and subset AR orders, followed by the full and subset MA orders, followed by a 1 if a constant term is in the model or a 0 if it is not.
coeffs An array containing starting values for the coefficients that are included in the model in the order full AR, subset AR, full MA, subset MA and the mean of {y}.
lags An array containing the lags (if any) in the model. If both the subset AR and MA orders are zero, no array called {lags} need be formed, but an argument must be included.
back An integer containing the number of back forecasts to used in determining initial values in the recursion used in evaluating the sum of squares of residuals functions $(>=0)$.
maxit An integer containing the number of iterations to allow in the estimation procedure. If {maxit} is negative, then –{maxit} iterations are allowed and the values of the coefficients for the successive iterations are displayed on the screen. If {maxit} is 1 then {SEASEST} only evaluates {rvar} and {tsl{sds}}.
eps Real scalar containing a convergence criterion. If the maximum value of successive iterates differs by less than {eps}, then {SEASEST} judges that the algorithm has converged.

Value

seasest returns a list containing the following five elements:
coeffs Array containing the final values reached for the parameters in the iterative process. {coeffs} is not changed from input if {maxit}$=1$.
e Array of length $n$ containing the one step ahead prediction errors corresponding to the $n$ values of {x}.
ier An integer variable indicating whether or not convergence was achieved (0 means yes, 1 means no), if a singular matrix was encountered (2), or whether the algorithm could not continue even though convergence was no reached (3 or 4). If this final alternative happens, different starting values or convergence criteria may lead to convergence.
rv Real scalar containing an estimate of the error variance.
se An array containing the standard errors of the estimates.


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