# NAG CL Interfaceg05pjc (times_​mv_​varma)

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## 1Purpose

g05pjc generates a realization of a multivariate time series from a vector autoregressive moving average (VARMA) model. The realization may be continued or a new realization generated at subsequent calls to g05pjc.

## 2Specification

 #include
 void g05pjc (Nag_OrderType order, Nag_ModeRNG mode, Integer n, Integer k, const double xmean[], Integer p, const double phi[], Integer q, const double theta[], const double var[], Integer pdv, double r[], Integer lr, Integer state[], double x[], Integer pdx, NagError *fail)
The function may be called by the names: g05pjc, nag_rand_times_mv_varma or nag_rand_varma.

## 3Description

Let the vector ${X}_{t}={\left({x}_{1t},{x}_{2t},\dots ,{x}_{kt}\right)}^{\mathrm{T}}$, denote a $k$-dimensional time series which is assumed to follow a vector autoregressive moving average (VARMA) model of the form:
 $Xt-μ= ϕ1(Xt-1-μ)+ϕ2(Xt-2-μ)+⋯+ϕp(Xt-p-μ)+ εt-θ1εt-1-θ2εt-2-⋯-θqεt-q$ (1)
where ${\epsilon }_{t}={\left({\epsilon }_{1t},{\epsilon }_{2t},\dots ,{\epsilon }_{kt}\right)}^{\mathrm{T}}$, is a vector of $k$ residual series assumed to be Normally distributed with zero mean and covariance matrix $\Sigma$. The components of ${\epsilon }_{t}$ are assumed to be uncorrelated at non-simultaneous lags. The ${\varphi }_{i}$'s and ${\theta }_{j}$'s are $k×k$ matrices of parameters. $\left\{{\varphi }_{i}\right\}$, for $\mathit{i}=1,2,\dots ,p$, are called the autoregressive (AR) parameter matrices, and $\left\{{\theta }_{j}\right\}$, for $\mathit{j}=1,2,\dots ,q$, the moving average (MA) parameter matrices. The parameters in the model are thus the $p$ $k×k$ $\varphi$-matrices, the $q$ $k×k$ $\theta$-matrices, the mean vector $\mu$ and the residual error covariance matrix $\Sigma$. Let
 $A(ϕ)= [ ϕ1 I 0 . . . 0 ϕ2 0 I 0 . . 0 . . . . . . ϕp-1 0 . . . 0 I ϕp 0 . . . 0 0 ] pk×pk and B(θ)= [ θ1 I 0 . . . 0 θ2 0 I 0 . . 0 . . . . . . θq- 1 0 . . . 0 I θq 0 . . . 0 0 ] qk×qk$
where $I$ denotes the $k×k$ identity matrix.
The model (1) must be both stationary and invertible. The model is said to be stationary if the eigenvalues of $A\left(\varphi \right)$ lie inside the unit circle and invertible if the eigenvalues of $B\left(\theta \right)$ lie inside the unit circle.
For $k\ge 6$ the VARMA model (1) is recast into state space form and a realization of the state vector at time zero computed. For all other cases the function computes a realization of the pre-observed vectors ${X}_{0},{X}_{-1},\dots ,{X}_{1-p}$, ${\epsilon }_{0},{\epsilon }_{-1},\dots ,{\epsilon }_{1-q}$, from (1), see Shea (1988). This realization is then used to generate a sequence of successive time series observations. Note that special action is taken for pure MA models, that is for $p=0$.
At your request a new realization of the time series may be generated more efficiently using the information in a reference vector created during a previous call to g05pjc. See the description of the argument mode in Section 5 for details.
The function returns a realization of ${X}_{1},{X}_{2},\dots ,{X}_{n}$. On a successful exit, the recent history is updated and saved in the array r so that g05pjc may be called again to generate a realization of ${X}_{n+1},{X}_{n+2},\dots$, etc. See the description of the argument mode in Section 5 for details.
Further computational details are given in Shea (1988). Note, however, that g05pjc uses a spectral decomposition rather than a Cholesky factorization to generate the multivariate Normals. Although this method involves more multiplications than the Cholesky factorization method and is thus slightly slower it is more stable when faced with ill-conditioned covariance matrices. A method of assigning the AR and MA coefficient matrices so that the stationarity and invertibility conditions are satisfied is described in Barone (1987).
One of the initialization functions g05kfc (for a repeatable sequence if computed sequentially) or g05kgc (for a non-repeatable sequence) must be called prior to the first call to g05pjc.
Barone P (1987) A method for generating independent realisations of a multivariate normal stationary and invertible ARMA$\left(p,q\right)$ process J. Time Ser. Anal. 8 125–130
Shea B L (1988) A note on the generation of independent realisations of a vector autoregressive moving average process J. Time Ser. Anal. 9 403–410

## 5Arguments

1: $\mathbf{order}$Nag_OrderType Input
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by ${\mathbf{order}}=\mathrm{Nag_RowMajor}$. See Section 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: ${\mathbf{order}}=\mathrm{Nag_RowMajor}$ or $\mathrm{Nag_ColMajor}$.
2: $\mathbf{mode}$Nag_ModeRNG Input
On entry: a code for selecting the operation to be performed by the function.
${\mathbf{mode}}=\mathrm{Nag_InitializeReference}$
Set up reference vector and compute a realization of the recent history.
${\mathbf{mode}}=\mathrm{Nag_GenerateFromReference}$
Generate terms in the time series using reference vector set up in a prior call to g05pjc.
${\mathbf{mode}}=\mathrm{Nag_InitializeAndGenerate}$
Combine the operations of ${\mathbf{mode}}=\mathrm{Nag_InitializeReference}$ and $\mathrm{Nag_GenerateFromReference}$.
${\mathbf{mode}}=\mathrm{Nag_ReGenerateFromReference}$
A new realization of the recent history is computed using information stored in the reference vector, and the following sequence of time series values are generated.
If ${\mathbf{mode}}=\mathrm{Nag_GenerateFromReference}$ or $\mathrm{Nag_ReGenerateFromReference}$, you must ensure that the reference vector r and the values of k, p, q, xmean, phi, theta, var and pdv have not been changed between calls to g05pjc.
Constraint: ${\mathbf{mode}}=\mathrm{Nag_InitializeReference}$, $\mathrm{Nag_GenerateFromReference}$, $\mathrm{Nag_InitializeAndGenerate}$ or $\mathrm{Nag_ReGenerateFromReference}$.
3: $\mathbf{n}$Integer Input
On entry: $n$, the number of observations to be generated.
Constraint: ${\mathbf{n}}\ge 0$.
4: $\mathbf{k}$Integer Input
On entry: $k$, the dimension of the multivariate time series.
Constraint: ${\mathbf{k}}\ge 1$.
5: $\mathbf{xmean}\left[{\mathbf{k}}\right]$const double Input
On entry: $\mu$, the vector of means of the multivariate time series.
6: $\mathbf{p}$Integer Input
On entry: $p$, the number of autoregressive parameter matrices.
Constraint: ${\mathbf{p}}\ge 0$.
7: $\mathbf{phi}\left[{\mathbf{k}}×{\mathbf{k}}×{\mathbf{p}}\right]$const double Input
On entry: must contain the elements of the ${\mathbf{p}}×{\mathbf{k}}×{\mathbf{k}}$ autoregressive parameter matrices of the model, ${\varphi }_{1},{\varphi }_{2},\dots ,{\varphi }_{p}$. The $\left(i,j\right)$th element of ${\varphi }_{\mathit{l}}$ is stored in ${\mathbf{phi}}\left[\left(\mathit{l}-1\right)×k×k+\left(j-1\right)×k+i-1\right]$, for $\mathit{l}=1,2,\dots ,p$, $i=1,2,\dots ,k$ and $j=1,2,\dots ,k$.
Constraint: the elements of phi must satisfy the stationarity condition.
8: $\mathbf{q}$Integer Input
On entry: $q$, the number of moving average parameter matrices.
Constraint: ${\mathbf{q}}\ge 0$.
9: $\mathbf{theta}\left[{\mathbf{k}}×{\mathbf{k}}×{\mathbf{q}}\right]$const double Input
On entry: must contain the elements of the ${\mathbf{q}}×{\mathbf{k}}×{\mathbf{k}}$ moving average parameter matrices of the model, ${\theta }_{1},{\theta }_{2},\dots ,{\theta }_{q}$. The $\left(i,j\right)$th element of ${\theta }_{\mathit{l}}$ is stored in ${\mathbf{theta}}\left[\left(\mathit{l}-1\right)×k×k+\left(\mathit{j}-1\right)×k+\mathit{i}-1\right]$, for $\mathit{l}=1,2,\dots ,q$, $\mathit{i}=1,2,\dots ,k$ and $\mathit{j}=1,2,\dots ,k$.
Constraint: the elements of theta must be within the invertibility region.
10: $\mathbf{var}\left[\mathit{dim}\right]$const double Input
Note: the dimension, dim, of the array var must be at least ${\mathbf{pdv}}×{\mathbf{k}}$.
where ${\mathbf{VAR}}\left(i,j\right)$ appears in this document, it refers to the array element
• ${\mathbf{var}}\left[\left(j-1\right)×{\mathbf{pdv}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{var}}\left[\left(i-1\right)×{\mathbf{pdv}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: ${\mathbf{VAR}}\left(\mathit{i},\mathit{j}\right)$ must contain the ($\mathit{i},\mathit{j}$)th element of $\Sigma$, for $\mathit{i}=1,2,\dots ,{\mathbf{k}}$ and $\mathit{j}=1,2,\dots ,{\mathbf{k}}$. Only the lower triangle is required.
Constraint: the elements of var must be such that $\Sigma$ is positive semidefinite.
11: $\mathbf{pdv}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array var.
Constraint: ${\mathbf{pdv}}\ge {\mathbf{k}}$.
12: $\mathbf{r}\left[{\mathbf{lr}}\right]$double Communication Array
On entry: if ${\mathbf{mode}}=\mathrm{Nag_GenerateFromReference}$ or $\mathrm{Nag_ReGenerateFromReference}$, the array r as output from the previous call to g05pjc must be input without any change.
If ${\mathbf{mode}}=\mathrm{Nag_InitializeReference}$ or $\mathrm{Nag_InitializeAndGenerate}$, the contents of r need not be set.
On exit: information required for any subsequent calls to the function with ${\mathbf{mode}}=\mathrm{Nag_GenerateFromReference}$ or $\mathrm{Nag_ReGenerateFromReference}$. See Section 9.
13: $\mathbf{lr}$Integer Input
On entry: the dimension of the array r.
Constraints:
• if ${\mathbf{k}}\ge 6$, ${\mathbf{lr}}\ge \left(5{\mathit{r}}^{2}+1\right)×{{\mathbf{k}}}^{2}+\left(4\mathit{r}+3\right)×{\mathbf{k}}+4$;
• if ${\mathbf{k}}<6$, ${\mathbf{lr}}\ge \left({\left({\mathbf{p}}+{\mathbf{q}}\right)}^{2}+1\right)×{{\mathbf{k}}}^{2}+\phantom{\rule{0ex}{0ex}}\left(4×\left({\mathbf{p}}+{\mathbf{q}}\right)+3\right)×{\mathbf{k}}+\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{k}}\mathit{r}\left({\mathbf{k}}\mathit{r}+2\right),{{\mathbf{k}}}^{2}{\left({\mathbf{p}}+{\mathbf{q}}\right)}^{2}+\mathit{l}\left(\mathit{l}+3\right)+{{\mathbf{k}}}^{2}\left({\mathbf{q}}+1\right)\right)+4$.
Where $\mathit{r}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{p}},{\mathbf{q}}\right)$ and if ${\mathbf{p}}=0$, $\mathit{l}={\mathbf{k}}\left({\mathbf{k}}+1\right)/2$, or if ${\mathbf{p}}\ge 1$, $\mathit{l}={\mathbf{k}}\left({\mathbf{k}}+1\right)/2+\left({\mathbf{p}}-1\right){{\mathbf{k}}}^{2}$.
See Section 9 for some examples of the required size of the array r.
14: $\mathbf{state}\left[\mathit{dim}\right]$Integer Communication Array
Note: the dimension, $\mathit{dim}$, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument state in the previous call to nag_rand_init_repeatable (g05kfc) or nag_rand_init_nonrepeatable (g05kgc).
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
15: $\mathbf{x}\left[\mathit{dim}\right]$double Output
Note: the dimension, dim, of the array x must be at least
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdx}}×{\mathbf{n}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{k}}×{\mathbf{pdx}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
where ${\mathbf{X}}\left(i,t\right)$ appears in this document, it refers to the array element
• ${\mathbf{x}}\left[\left(t-1\right)×{\mathbf{pdx}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{x}}\left[\left(i-1\right)×{\mathbf{pdx}}+t-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On exit: ${\mathbf{X}}\left(\mathit{i},\mathit{t}\right)$ will contain a realization of the $\mathit{i}$th component of ${X}_{\mathit{t}}$, for $\mathit{i}=1,2,\dots ,k$ and $\mathit{t}=1,2,\dots ,n$.
16: $\mathbf{pdx}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array x.
Constraints:
• if ${\mathbf{order}}=\mathrm{Nag_ColMajor}$, ${\mathbf{pdx}}\ge {\mathbf{k}}$;
• if ${\mathbf{order}}=\mathrm{Nag_RowMajor}$, ${\mathbf{pdx}}\ge {\mathbf{n}}$.
17: $\mathbf{fail}$NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

## 6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument $⟨\mathit{\text{value}}⟩$ had an illegal value.
NE_CLOSE_TO_STATIONARITY
The reference vector cannot be computed because the AR parameters are too close to the boundary of the stationarity region.
NE_INT
On entry, ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{k}}\ge 1$.
On entry, lr is not large enough, ${\mathbf{lr}}=⟨\mathit{\text{value}}⟩$: minimum length required $\text{}=⟨\mathit{\text{value}}⟩$.
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}\ge 0$.
On entry, ${\mathbf{p}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{p}}\ge 0$.
On entry, ${\mathbf{pdv}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdv}}>0$.
On entry, ${\mathbf{pdx}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdx}}>0$.
On entry, ${\mathbf{q}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{q}}\ge 0$.
NE_INT_2
On entry, ${\mathbf{pdv}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdv}}\ge {\mathbf{k}}$.
On entry, ${\mathbf{pdx}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdx}}\ge {\mathbf{k}}$.
On entry, ${\mathbf{pdx}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdx}}\ge {\mathbf{n}}$.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_INVALID_STATE
On entry, state vector has been corrupted or not initialized.
NE_INVERTIBILITY
On entry, the moving average parameter matrices are such that the model is non-invertible.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_POS_DEF
On entry, the covariance matrix var is not positive semidefinite to machine precision.
NE_PREV_CALL
k is not the same as when r was set up in a previous call.
Previous value of ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$.
NE_STATIONARY_AR
On entry, the AR parameters are outside the stationarity region.
NE_TOO_MANY_ITER
An excessive number of iterations were required by the NAG function used to evaluate the eigenvalues of the covariance matrix.
An excessive number of iterations were required by the NAG function used to evaluate the eigenvalues of the matrices used to test for stationarity or invertibility.
An excessive number of iterations were required by the NAG function used to evaluate the eigenvalues stored in the reference vector.

## 7Accuracy

The accuracy is limited by the matrix computations performed, and this is dependent on the condition of the parameter and covariance matrices.

## 8Parallelism and Performance

g05pjc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
g05pjc 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 function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

Note that, in reference to ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_INVERTIBILITY, g05pjc will permit moving average parameters on the boundary of the invertibility region.
The elements of r contain amongst other information details of the spectral decompositions which are used to generate future multivariate Normals. Note that these eigenvectors may not be unique on different machines. For example the eigenvectors corresponding to multiple eigenvalues may be permuted. Although an effort is made to ensure that the eigenvectors have the same sign on all machines, differences in the signs may theoretically still occur.
The following table gives some examples of the required size of the array r, specified by the argument lr, for $k=1,2$ or $3$, and for various values of $p$ and $q$.
$\mathbit{q}$
0 1 2 3
$\mathbit{p}$ 13 20 31 46
0 36 56 92 144
85 124 199 310
19 30 45 64
1 52 88 140 208
115 190 301 448
35 50 69 92
2 136 188 256 340
397 508 655 838
57 76 99 126
3 268 336 420 520
877 1024 1207 1426
Note that g13dxc may be used to check whether a VARMA model is stationary and invertible.
The time taken depends on the values of $p$, $q$ and especially $n$ and $k$.

## 10Example

This program generates two realizations, each of length $48$, from the bivariate AR(1) model
 $Xt-μ=ϕ1(Xt-1-μ)+εt$
with
 $ϕ1=[ 0.80 0.07 0.00 0.58 ] ,$
 $μ=[ 5.00 9.00 ] ,$
and
 $Σ=[ 2.97 0 0.64 5.38 ] .$
The pseudorandom number generator is initialized by a call to g05kfc. Then, in the first call to g05pjc, ${\mathbf{mode}}=\mathrm{Nag_InitializeAndGenerate}$ in order to set up the reference vector before generating the first realization. In the subsequent call ${\mathbf{mode}}=\mathrm{Nag_ReGenerateFromReference}$ and a new recent history is generated and used to generate the second realization.

### 10.1Program Text

Program Text (g05pjce.c)

### 10.2Program Data

Program Data (g05pjce.d)

### 10.3Program Results

Program Results (g05pjce.r)