NAG Library Routine Document
G13DJF
1 Purpose
G13DJF computes forecasts of a multivariate time series. It is assumed that a vector ARMA model has already been fitted to the appropriately differenced/transformed time series using
G13DDF.
The standard deviations of the forecast errors are also returned. A reference vector is set up so that, should future series values become available, the forecasts and their standard errors may be updated by calling
G13DKF.
2 Specification
SUBROUTINE G13DJF ( |
K, N, Z, KMAX, TR, ID, DELTA, IP, IQ, MEAN, PAR, LPAR, QQ, V, LMAX, PREDZ, SEFZ, REF, LREF, WORK, LWORK, IWORK, LIWORK, IFAIL) |
INTEGER |
K, N, KMAX, ID(K), IP, IQ, LPAR, LMAX, LREF, LWORK, IWORK(LIWORK), LIWORK, IFAIL |
REAL (KIND=nag_wp) |
Z(KMAX,N), DELTA(KMAX,*), PAR(LPAR), QQ(KMAX,K), V(KMAX,*), PREDZ(KMAX,LMAX), SEFZ(KMAX,LMAX), REF(LREF), WORK(LWORK) |
CHARACTER(1) |
TR(K), MEAN |
|
3 Description
Let the vector
, for
, denote a
-dimensional time series for which forecasts of
are required. Let
be defined as follows:
where
is the differencing operator applied to the
th series and where
is equal to either
,
or
depending on whether or not a transformation was required to stabilize the variance before fitting the model.
If the order of differencing required for the th series is , then the differencing operator for the th series is defined by where is the backward shift operator; that is, . The differencing parameters , for and , must be supplied by you. If the th series does not require differencing, then .
is assumed to follow a multivariate ARMA model of the form:
where
, for
, is a vector of
residual series assumed to be Normally distributed with zero mean and positive definite covariance matrix
. The components of
are assumed to be uncorrelated at non-simultaneous lags. The
and
are
by
matrices of parameters. The matrices
, for
, are the autoregressive (AR) parameter matrices, and the matrices
, for
, the moving average (MA) parameter matrices. The parameters in the model are thus the
(
by
)
-matrices, the
(
by
)
-matrices, the mean vector
and the residual error covariance matrix
. The ARMA model
(1) must be both stationary and invertible; see
G13DXF for a method of checking these conditions.
The ARMA model
(1) may be rewritten as
where
and
are the autoregressive and moving average polynomials and
denotes the
by
diagonal matrix whose
th diagonal elements is
and
.
This may be rewritten as
or
where
and
is a vector of length
.
Forecasts are computed using a multivariate version of the procedure described in
Box and Jenkins (1976). If
denotes the forecast of
, then
is taken to be that linear function of
which minimizes the elements of
where
is the forecast error.
is referred to as the linear minimum mean square error forecast of
.
The linear predictor which minimizes the mean square error may be expressed as
The forecast error at
for lead
is then
Let
, for
. Unless
the routine requires estimates of
, for
, which are obtainable from
G13DDF.
The terms
are assumed to be zero, for
. You may use
G13DKF to update these
forecasts should further observations,
, become available. Note that when
or more further observations are available then G13DJF must be used to produce new forecasts for
, should they be required.
When a transformation has been used the forecasts and their standard errors are suitably modified to give results in terms of the original series,
; see
Granger and Newbold (1976).
4 References
Box G E P and Jenkins G M (1976) Time Series Analysis: Forecasting and Control (Revised Edition) Holden–Day
Granger C W J and Newbold P (1976) Forecasting transformed series J. Roy. Statist. Soc. Ser. B 38 189–203
Wei W W S (1990) Time Series Analysis: Univariate and Multivariate Methods Addison–Wesley
5 Parameters
The output quantities
K,
N,
KMAX,
IP,
IQ,
PAR,
NPAR,
QQ and
V from
G13DDF
are suitable for input to G13DJF.
- 1: K – INTEGERInput
On entry: , the dimension of the multivariate time series.
Constraint:
.
- 2: N – INTEGERInput
On entry: , the number of observations in the series, , prior to differencing.
Constraint:
.
The total number of observations must exceed the total number of parameters in the model; that is
- if , ;
- if , ,
(see the parameters
IP,
IQ and
MEAN).
- 3: Z(KMAX,N) – REAL (KIND=nag_wp) arrayInput
On entry: must contain, , the th component of , for and .
Constraints:
- if , ;
- if , , for and .
- 4: KMAX – INTEGERInput
On entry: the first dimension of the arrays
Z,
DELTA,
QQ,
V,
PREDZ and
SEFZ as declared in the (sub)program from which G13DJF is called.
Constraint:
.
- 5: TR(K) – CHARACTER(1) arrayInput
On entry:
indicates whether the
th time series is to be transformed, for
.
- No transformation is used.
- A log transformation is used.
- A square root transformation is used.
Constraint:
, or , for .
- 6: ID(K) – INTEGER arrayInput
On entry: must specify, , the order of differencing required for the th series.
Constraint:
, for .
- 7: DELTA(KMAX,) – REAL (KIND=nag_wp) arrayInput
-
Note: the second dimension of the array
DELTA
must be at least
, where
.
On entry: if
, then
must be set equal to
, for
and
.
If
,
DELTA is not referenced.
- 8: IP – INTEGERInput
On entry: , the number of AR parameter matrices.
Constraint:
.
- 9: IQ – INTEGERInput
On entry: , the number of MA parameter matrices.
Constraint:
.
- 10: MEAN – CHARACTER(1)Input
On entry: , if components of have been estimated and , if all elements of are to be taken as zero.
Constraint:
or .
- 11: PAR(LPAR) – REAL (KIND=nag_wp) arrayInput
On entry: must contain the parameter estimates read in row by row in the order
,
,
.
Thus,
- if ,
must be set equal to an estimate of the th element of , for , and ;
- if ,
must be set equal to an estimate of the th element of , for , and ;
- if ,
must be set equal to an estimate of the th component of , for .
Constraint:
the first
elements of
PAR must satisfy the stationarity condition and the next
elements of
PAR must satisfy the invertibility condition.
- 12: LPAR – INTEGERInput
On entry: the dimension of the array
PAR as declared in the (sub)program from which G13DJF is called.
Constraints:
- if , ;
- if , .
- 13: QQ(KMAX,K) – REAL (KIND=nag_wp) arrayInput/Output
On entry: must contain an estimate of the th element of . The lower triangle only is needed.
Constraint:
must be positive definite.
On exit: if , then the upper triangle is set equal to the lower triangle.
- 14: V(KMAX,) – REAL (KIND=nag_wp) arrayInput
-
Note: the second dimension of the array
V
must be at least
, where
.
On entry:
must contain an estimate of the
th component of
, for
and
.
- 15: LMAX – INTEGERInput
On entry: the number, , of forecasts required.
Constraint:
.
- 16: PREDZ(KMAX,LMAX) – REAL (KIND=nag_wp) arrayOutput
On exit: contains the forecast of , for and .
- 17: SEFZ(KMAX,LMAX) – REAL (KIND=nag_wp) arrayOutput
On exit: contains an estimate of the standard error of the forecast of , for and .
- 18: REF(LREF) – REAL (KIND=nag_wp) arrayOutput
On exit: the reference vector which may be used to update forecasts using
G13DKF. The first
elements contain the
weight matrices,
. The next
elements contain the forecasts of the transformed series
and the next
contain the variances of the forecasts of the transformed variables. The last
K elements are used to store the transformations for the series.
- 19: LREF – INTEGERInput
On entry: the dimension of the array
REF as declared in the (sub)program from which G13DJF is called.
Constraint:
.
- 20: WORK(LWORK) – REAL (KIND=nag_wp) arrayWorkspace
- 21: LWORK – INTEGERInput
On entry: the dimension of the array
WORK as declared in the (sub)program from which G13DJF is called.
Constraint:
if and , for , .
- 22: IWORK(LIWORK) – INTEGER arrayWorkspace
- 23: LIWORK – INTEGERInput
On entry: the dimension of the array
IWORK as declared in the (sub)program from which G13DJF is called.
Constraint:
.
- 24: IFAIL – INTEGERInput/Output
-
On entry:
IFAIL must be set to
,
. If you are unfamiliar with this parameter you should refer to
Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
is recommended. If the output of error messages is undesirable, then the value
is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is
.
When the value is used it is essential to test the value of IFAIL on exit.
On exit:
unless the routine detects an error or a warning has been flagged (see
Section 6).
6 Error Indicators and Warnings
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
On entry, | , |
or | , |
or | , |
or | for some , |
or | for some , |
or | , |
or | , |
or | or , |
or | , and , |
or | and , |
or | , and , |
or | and , |
or | , |
or | , |
or | LWORK is too small, |
or | LIWORK is too small. |
-
On entry, | at least one of the first elements of TR is not equal to 'N', 'L' or 'S'. |
On entry, one or more of the transformations requested cannot be computed; that is, you may be trying to log or square-root a series, some of whose values are negative.
-
On entry, either
QQ is not positive definite or the autoregressive parameter matrices are extremely close to or outside the stationarity region, or the moving average parameter matrices are extremely close to or outside the invertibility region. To proceed, you must supply different parameter estimates in the arrays
PAR and
QQ.
This is an unlikely exit brought about by an excessive number of iterations being needed to evaluate the eigenvalues of the matrices required to check for stationarity and invertibility; see
G13DXF. All output parameters are undefined.
This is an unlikely exit which could occur if
QQ is nearly non positive definite. In this case the standard deviations of the forecast errors may be non-positive. To proceed, you must supply different parameter estimates in the array
QQ.
This is an unlikely exit. For one of the series, overflow will occur if the forecasts are computed. You should check whether the transformations requested in the array
TR are sensible. All output parameters are undefined.
7 Accuracy
The matrix computations are believed to be stable.
The same differencing operator does not have to be applied to all the series. For example, suppose we have
, and wish to apply the second order differencing operator
to the first series and the first-order differencing operator
to the second series:
Then
,
, and
Note: although differencing may already have been applied prior to the model fitting stage, the differencing parameters supplied in
DELTA are part of the model definition and are still required by this routine to produce the forecasts.
G13DJF should not be used when the moving average parameters lie close to the boundary of the invertibility region. The routine does test for both invertibility and stationarity but if in doubt, you may use
G13DXF, before calling this routine, to check that the VARMA model being used is invertible.
On a successful exit, the output quantities
K,
LMAX,
KMAX,
REF and
LREF will be suitable for input to
G13DKF.
9 Example
This example computes forecasts of the next five values in two series each of length
. No transformation is to be used and no differencing is to be applied to either of the series.
G13DDF
is first called to fit an AR(1) model to the series. The mean vector
is to be estimated and
constrained to be zero.
9.1 Program Text
Program Text (g13djfe.f90)
9.2 Program Data
Program Data (g13djfe.d)
9.3 Program Results
Program Results (g13djfe.r)