g13adf calculates preliminary estimates of the parameters of an autoregressive integrated moving average (ARIMA) model from the autocorrelation function of the appropriately differenced times series.

2 Specification

Fortran Interface

Subroutine g13adf (

mr, r, nk, xv, npar, wa, nwa, par, rv, isf, ifail)

Integer, Intent (In)	::	mr(7), nk, npar, nwa
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	isf(4)
Real (Kind=nag_wp), Intent (In)	::	r(nk), xv
Real (Kind=nag_wp), Intent (Out)	::	wa(nwa), par(npar), rv

C Header Interface

#include <nag.h>

void	g13adf_ (const Integer mr[], const double r[], const Integer nk, const double xv, const Integer npar, double wa[], const Integer nwa, double par[], double rv, Integer isf[], Integer ifail)

The routine may be called by the names g13adf or nagf_tsa_uni_arima_prelim.

3 Description

Preliminary estimates of the

p

non-seasonal autoregressive parameters

ϕ_{1}, ϕ_{2}, \dots, ϕ_{p}

and the

q

non-seasonal moving average parameters

θ_{1}, θ_{2}, \dots, θ_{q}

may be obtained from the sample autocorrelations relating to lags

1

p + q

, i.e.,

r_{1}, \dots, r_{p + q}

, of the differenced

\nabla^{d} \nabla_{s}^{D} x_{t}

, where

x_{t}

is assumed to follow a (possibly) seasonal ARIMA model (see Section 3 in g13aef for the specification of an ARIMA model).

Taking

r_{0} = 1

and

r_{- k} = r_{k}

, the

ϕ_{i}

, for

i = 1, 2, \dots, p

are the solutions to the equations

r_{q + i - 1} ϕ_{1} + r_{q + i - 2} ϕ_{2} + \dots + r_{q + i - p} ϕ_{p} = r_{q + i}, i = 1, 2, \dots, p .

The

θ_{j}

, for

j = 1, 2, \dots, q

, are obtained from the solutions to the equations

c_{j} = τ_{0} τ_{j} + τ_{1} τ_{j + 1} + \dots + τ_{q + j} τ_{q}, j = 0, 1, \dots, q

(Cramer Wold-factorization), by setting

θ_{j} = - \frac{τ_{j}}{τ_{0}},

where

c_{j}

are the ‘covariances’ modified in a two stage process by the autoregressive parameters.

Stage 1:

\begin{array}{l} d_{j} = r_{j} - ϕ_{1} r_{j - 1} - \dots - ϕ_{p} r_{j - p}, & j = 0, 1, \dots, q; \\ d_{j} = 0, & j = q + 1, q + 2, \dots, p + q . \end{array}

Stage 2:

c_{j} = d_{j} - ϕ_{1} d_{j + 1} - ϕ_{2} d_{j + 2} - \dots - ϕ_{p} d_{j + p}, j = 0, 1, \dots, q .

The

P

seasonal autoregressive parameters

Φ_{1}, Φ_{2}, \dots, Φ_{P}

and the

Q

seasonal moving average parameters

Θ_{1}, Θ_{2}, \dots, Θ_{Q}

are estimated in the same way as the non-seasonal parameters, but each

r_{j}

is replaced in the calculation by

r_{s \times j}

, where

s

is the seasonal period.

An estimate of the residual variance is obtained by successively reducing the sample variance, first for non-seasonal, and then for seasonal, parameter estimates. If moving average parameters are estimated, the variance is reduced by a multiplying factor of

τ_{0}^{2}

, but otherwise by

c_{0}

4 References

Box G E P and Jenkins G M (1976) Time Series Analysis: Forecasting and Control (Revised Edition) Holden–Day

5 Arguments

1: $mr (7)$ – Integer array Input

On entry: the orders vector

(p, d, q, P, D, Q, s)

of the ARIMA model whose parameters are to be estimated.

p

q

P

and

Q

refer respectively to the number of autoregressive

(ϕ)

, moving average

(θ)

, seasonal autoregressive

(Φ)

and seasonal moving average

(Θ)

parameters.

d

D

and

s

refer respectively to the order of non-seasonal differencing, the order of seasonal differencing and the seasonal period.

Constraints:

$p, d, q, P, D, Q, s \geq 0$ ;
$p + q + P + Q > 0$ ;
$s \neq 1$ ;
if $s = 0$ , $P + D + Q = 0$ ;
if $s > 1$ , $P + D + Q > 0$ .

2: $r (nk)$ – Real (Kind=nag_wp) array Input

On entry: the autocorrelations (starting at lag

1

), which must have been calculated after the time series has been appropriately differenced.

Constraint:

- 1.0 \leq r (i) \leq 1.0

, for

i = 1, 2, \dots, nk

3: $nk$ – Integer Input

On entry: the maximum lag of the autocorrelations in array r.

Constraint:

nk \geq \max (p + q, s \times (P + Q))

4: $xv$ – Real (Kind=nag_wp) Input

On entry: the series sample variance, calculated after appropriate differencing has been applied to the series.

Constraint:

xv > 0.0

5: $npar$ – Integer Input

On entry: the exact number of parameters specified in the model by array mr.

Constraint:

npar = p + q + P + Q

6: $wa (nwa)$ – Real (Kind=nag_wp) array Workspace

7: $nwa$ – Integer Input

On entry: the amount of workspace available.

Constraint: if

mr = (p, d, q, P, D, Q, s)

and

p^{'} = \max (p, P)

and

q^{'} = \max (q, Q)

nwa \geq \max ({p^{'}}^{2} + p^{'}, 4 (q^{'} + 1))

8: $par (npar)$ – Real (Kind=nag_wp) array Output

On exit: the first npar elements of par contain the preliminary estimates of the ARIMA model parameters, in standard order.

9: $rv$ – Real (Kind=nag_wp) Output

On exit: an estimate of the residual variance of the preliminarily estimated model.

10: $isf (4)$ – Integer array Output

On exit: contains success/failure indicators, one for each of the four types of parameter (autoregressive, moving average, seasonal autoregressive, seasonal moving average).

The indicator has the interpretation:

$0$	No parameter of this type is in the model.
$1$	Parameters of this type appear in the model and satisfactory preliminary estimates of this type were obtained.
$- 1$	Parameters of this type appear in the model but satisfactory preliminary estimates of this type were not obtainable. The estimates of this type of parameter were set to $0.0$ in array par.

11: $ifail$ – Integer Input/Output

On entry: ifail must be set to

0

- 1 or 1

. If you are unfamiliar with this argument you should refer to Section 4 in the Introduction to the NAG Library FL Interface for details.

For environments where it might be inappropriate to halt program execution when an error is detected, the value

- 1 or 1

is recommended. If the output of error messages is undesirable, then the value

1

is recommended. Otherwise, if you are not familiar with this argument, the recommended value is

0

. 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

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail = 1$: The orders vector mr is invalid.

$ifail = 2$: On entry, $nk = 〈value〉$ .
Constraint: $nk \geq \max (p + q, s \times (P + Q))$ .

$ifail = 3$: On entry, $r (〈value〉) = 〈value〉$ .
Constraint: $- 1.0 \leq r (i) \leq 1.0$ , for $i = 1, 2, \dots nk$ .

$ifail = 4$: On entry, $xv = 〈value〉$ .
Constraint: $xv > 0.0$ .

$ifail = 5$: On entry, $npar = 〈value〉$ .
Constraint: $npar = p + q + P + Q$ .

$ifail = 6$: On entry, $nwa = 〈value〉$ and the minimum size $required = 〈value〉$ .
Constraint: $nwa \geq \max ({p^{'}}^{2} + p^{'}, 4 (q^{'} + 1))$ , where $p^{'} = \max (p, P)$ and $q^{'} = \max (q, Q)$ .

$ifail = 7$: Satisfactory parameter estimates could not be obtained for all parameter types in the model. Inspect array isf for indicators of the parameter type(s) which could not estimated.

$ifail = - 99$: 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.

$ifail = - 399$: 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.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

The performance of the algorithm is conditioned by the roots of the autoregressive and moving average operators. If these are not close to unity in modulus, the errors,

e

, should satisfy

e < 100 ε

where

ε

is machine precision.

8 Parallelism and Performance

g13adf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

g13adf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

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 g13adf is approximately proportional to

(p^{3} + q^{2} + P^{3} + Q^{2}) .

10 Example

This example reads the sample autocorrelations to lag

40

and the sample variance of the lagged and doubly differenced series of airline passenger totals (Box and Jenkins example series G (see Box and Jenkins (1976))). Preliminary estimates of the parameters of the

(0, 1, 1, 0, 1, 1, 12)

model are obtained by a call to g13adf.

Interfaces: FL CL AD

NAG FL Interface Introduction

G13 (Tsa) Chapter Contents

G13 (Tsa) Chapter Introduction

g13ad: FL CL AD

NAG FL Interfaceg13adf (uni_​arima_​prelim)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
g13adf (uni_arima_prelim)