NAG FL Interface
g05phf (times_​arma)

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1 Purpose

g05phf generates a realization of a univariate time series from an autoregressive moving average (ARMA) model. The realization may be continued or a new realization generated at subsequent calls to g05phf.

2 Specification

Fortran Interface
Subroutine g05phf ( mode, n, xmean, ip, phi, iq, theta, avar, r, lr, state, var, x, ifail)
Integer, Intent (In) :: mode, n, ip, iq, lr
Integer, Intent (Inout) :: state(*), ifail
Real (Kind=nag_wp), Intent (In) :: xmean, phi(ip), theta(iq), avar
Real (Kind=nag_wp), Intent (Inout) :: r(lr)
Real (Kind=nag_wp), Intent (Out) :: var, x(n)
C Header Interface
#include <nag.h>
void  g05phf_ (const Integer *mode, const Integer *n, const double *xmean, const Integer *ip, const double phi[], const Integer *iq, const double theta[], const double *avar, double r[], const Integer *lr, Integer state[], double *var, double x[], Integer *ifail)
The routine may be called by the names g05phf or nagf_rand_times_arma.

3 Description

Let the vector xt, denote a time series which is assumed to follow an autoregressive moving average (ARMA) model of the form:
xt-μ= ϕ1(xt-1-μ)+ϕ2(xt-2-μ)++ϕp(xt-p-μ)+ εt-θ1εt-1-θ2εt-2--θqεt-q  
where εt, is a residual series of independent random perturbations assumed to be Normally distributed with zero mean and variance σ2. The parameters {ϕi}, for i=1,2,,p, are called the autoregressive (AR) parameters, and {θj}, for j=1,2,,q, the moving average (MA) parameters. The parameters in the model are thus the p ϕ values, the q θ values, the mean μ and the residual variance σ2.
g05phf sets up a reference vector containing initial values corresponding to a stationary position using the method described in Tunnicliffe–Wilson (1979). The routine can then return a realization of x1,x2,,xn. On a successful exit, the recent history is updated and saved in the reference vector r so that g05phf may be called again to generate a realization of xn+1,xn+2,, etc. See the description of the argument mode in Section 5 for details.
One of the initialization routines g05kff (for a repeatable sequence if computed sequentially) or g05kgf (for a non-repeatable sequence) must be called prior to the first call to g05phf.

4 References

Knuth D E (1981) The Art of Computer Programming (Volume 2) (2nd Edition) Addison–Wesley
Tunnicliffe–Wilson G (1979) Some efficient computational procedures for high order ARMA models J. Statist. Comput. Simulation 8 301–309

5 Arguments

1: mode Integer Input
On entry: a code for selecting the operation to be performed by the routine.
Set up reference vector only.
Generate terms in the time series using reference vector set up in a prior call to g05phf.
Set up reference vector and generate terms in the time series.
Constraint: mode=0, 1 or 2.
2: n Integer Input
On entry: n, the number of observations to be generated.
Constraint: n0.
3: xmean Real (Kind=nag_wp) Input
On entry: the mean of the time series.
4: ip Integer Input
On entry: p, the number of autoregressive coefficients supplied.
Constraint: ip0.
5: phi(ip) Real (Kind=nag_wp) array Input
On entry: the autoregressive coefficients of the model, ϕ1,ϕ2,,ϕp.
6: iq Integer Input
On entry: q, the number of moving average coefficients supplied.
Constraint: iq0.
7: theta(iq) Real (Kind=nag_wp) array Input
On entry: the moving average coefficients of the model, θ1,θ2,,θq.
8: avar Real (Kind=nag_wp) Input
On entry: σ2, the variance of the Normal perturbations.
Constraint: avar0.0.
9: r(lr) Real (Kind=nag_wp) array Communication Array
On entry: if mode=1, the reference vector from the previous call to g05phf.
On exit: the reference vector.
10: lr Integer Input
On entry: the dimension of the array r as declared in the (sub)program from which g05phf is called.
Constraint: lrip+iq+6+max(ip,iq+1).
11: state(*) Integer array Communication Array
Note: the actual argument supplied must be the array state supplied to the initialization routines g05kff or g05kgf.
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
12: var Real (Kind=nag_wp) Output
On exit: the proportion of the variance of a term in the series that is due to the moving-average (error) terms in the model. The smaller this is, the nearer is the model to non-stationarity.
13: x(n) Real (Kind=nag_wp) array Output
On exit: contains the next n observations from the time series.
14: ifail Integer Input/Output
On entry: ifail must be set to 0, −1 or 1 to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of 0 causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of −1 means that an error message is printed while a value of 1 means that it is not.
If halting is not appropriate, the value −1 or 1 is recommended. If message printing is undesirable, then the value 1 is recommended. Otherwise, the value 0 is recommended. When the value -1 or 1 is used it is essential to test the value of ifail on exit.
On exit: ifail=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry ifail=0 or −1, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
On entry, mode=value.
Constraint: mode=0, 1 or 2.
On entry, n=value.
Constraint: n0.
On entry, ip=value.
Constraint: ip0.
On entry, the AR parameters are outside the stationarity region.
On entry, iq=value.
Constraint: iq0.
On entry, avar=value.
Constraint: avar0.0.
ip or iq is not the same as when r was set up in a previous call.
Previous value of ip=value and ip=value.
Previous value of iq=value and iq=value.
Reference vector r has been corrupted or not initialized correctly.
On entry, lr is not large enough, lr=value: minimum length required =value.
On entry, state vector has been corrupted or not initialized.
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

Any errors in the reference vector's initial values should be very much smaller than the error term; see Tunnicliffe–Wilson (1979).

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
g05phf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The time taken by g05phf is essentially of order (ip) 2.
Note:  The reference vector, r, contains a copy of the recent history of the series. If attempting to re-initialize the series by calling g05kff or g05kgf a call to g05phf with mode=0 must also be made. In the repeatable case the calls to g05phf should be performed in the same order (at the same point(s) in simulation) every time g05kff is used. When the generator state is saved and restored using the argument state, the time series reference vector must be saved and restored as well.
The ARMA model for a time series can also be written as:
(xn-E) = A1 (xn-1-E) + + ANA (xn-NA-E) + B1 an + + BNB an-NB+1  
This is the form used in g05phf. This is related to the form given in Section 3 by:

10 Example

This example generates values for an autoregressive model given by
where εt is a series of independent random Normal perturbations with variance 1.0. The random number generators are initialized by g05kff and then g05phf is called to initialize a reference vector and generate a sample of ten observations.

10.1 Program Text

Program Text (g05phfe.f90)

10.2 Program Data

Program Data (g05phfe.d)

10.3 Program Results

Program Results (g05phfe.r)