# NAG CL Interfaceg13dsc (multi_​varma_​diag)

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## 1Purpose

g13dsc is a diagnostic checking function suitable for use after fitting a vector ARMA model to a multivariate time series using g13ddc. The residual cross-correlation matrices are returned along with an estimate of their asymptotic standard errors and correlations. Also, g13dsc calculates the modified Li–McLeod portmanteau statistic and its significance level for testing model adequacy.

## 2Specification

 #include
 void g13dsc (Integer k, Integer n, const double v[], Integer kmax, Integer ip, Integer iq, Integer m, const double par[], const Nag_Boolean parhld[], double qq[], Integer ishow, const char *outfile, double r0[], double r[], double rcm[], Integer pdrcm, double *chi, Integer *idf, double *siglev, NagError *fail)
The function may be called by the names: g13dsc, nag_tsa_multi_varma_diag or nag_tsa_varma_diagnostic.

## 3Description

Let ${W}_{\mathit{t}}={\left({w}_{1\mathit{t}},{w}_{2\mathit{t}},\dots ,{w}_{\mathit{k}\mathit{t}}\right)}^{\mathrm{T}}$, for $\mathit{t}=1,2,\dots ,n$, denote a vector of $k$ time series which is assumed to follow a multivariate ARMA model of the form
 $Wt-μ= ϕ1(Wt-1-μ)+ϕ2(Wt-2-μ)+⋯+ϕp(Wt-p-μ) +εt-θ1εt-1-θ2εt-2-⋯-θqεt-q,$ (1)
where ${\epsilon }_{\mathit{t}}={\left({\epsilon }_{1\mathit{t}},{\epsilon }_{2\mathit{t}},\dots ,{\epsilon }_{k\mathit{t}}\right)}^{\mathrm{T}}$, for $\mathit{t}=1,2,\dots ,n$, is a vector of $k$ residual series assumed to be Normally distributed with zero mean and positive definite covariance matrix $\Sigma$. The components of ${\epsilon }_{t}$ are assumed to be uncorrelated at non-simultaneous lags. The ${\varphi }_{i}$ and ${\theta }_{j}$ are $k×k$ matrices of parameters. $\left\{{\varphi }_{\mathit{i}}\right\}$, for $\mathit{i}=1,2,\dots ,p$, are called the autoregressive (AR) parameter matrices, and $\left\{{\theta }_{\mathit{i}}\right\}$, for $\mathit{i}=1,2,\dots ,q$, 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
 $A(ϕ)= [ ϕ1 I 0 . . . 0 ϕ2 0 I 0 . . 0 . . . . . . ϕp-1 0 . . . 0 I ϕp 0 . . . 0 0 ] pk×pk and B(θ)= [ θ1 I 0 . . . 0 θ2 0 I 0 . . 0 . . . . . . θq-1 0 . . . I θq 0 . . . . 0 ] qk×qk$
where $I$ denotes the $k×k$ identity matrix.
The ARMA model (1) 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. The ARMA model is assumed to be both stationary and invertible. Note that some of the elements of the $\varphi$- and/or $\theta$-matrices may have been fixed at pre-specified values (for example by calling g13ddc).
The estimated residual cross-correlation matrix at lag $l$ is defined to the $k×k$ matrix ${\stackrel{^}{R}}_{l}$ whose $\left(i,j\right)$th element is computed as
 $r ^ i j (l) = ∑ t = l + 1 n ( ε ^ i t - l - ε ¯ i ) ( ε ^ j t - ε ¯ j ) ∑ t = 1 n ( ε ^ i t - ε ¯ i ) 2 ∑ t = 1 n ( ε ^ j t - ε ¯ j ) 2 , l =0,1,…,i​ and ​j=1,2,…,k ,$
where ${\stackrel{^}{\epsilon }}_{it}$ denotes an estimate of the $t$th residual for the $i$th series ${\epsilon }_{it}$ and ${\overline{\epsilon }}_{i}=\sum _{t=1}^{n}{\stackrel{^}{\epsilon }}_{it}/n$. (Note that ${\stackrel{^}{R}}_{l}$ is an estimate of $E\left({\epsilon }_{t-l}{\epsilon }_{t}^{\mathrm{T}}\right)$, where $E$ is the expected value.)
A modified portmanteau statistic, ${Q}_{\left(m\right)}^{*}$, is calculated from the formula (see Li and McLeod (1981))
 $Q(m) * = k2 m(m+1) 2n + n ∑ l=1 m r^ (l)T ( R^ 0 −1 ⊗ R^ 0 −1 ) r^ (l) ,$
where $\otimes$ denotes Kronecker product, ${\stackrel{^}{R}}_{0}$ is the estimated residual cross-correlation matrix at lag zero and $\stackrel{^}{r}\left(l\right)=\mathrm{vec}\left({\stackrel{^}{R}}_{l}^{\mathrm{T}}\right)$, where $\text{vec}$ of a $k×k$ matrix is a vector with the $\left(i,j\right)$th element in position $\left(i-1\right)k+j$. $m$ denotes the number of residual cross-correlation matrices computed. (Advice on the choice of $m$ is given in Section 9.2.) Let ${l}_{C}$ denote the total number of ‘free’ parameters in the ARMA model excluding the mean, $\mu$, and the residual error covariance matrix $\Sigma$. Then, under the hypothesis of model adequacy, ${Q}_{\left(m\right)}^{*}$, has an asymptotic ${\chi }^{2}$-distribution on $m{k}^{2}-{l}_{C}$ degrees of freedom.
Let $\underline{\stackrel{^}{r}}=\left(\mathrm{vec}\left({R}_{1}^{\mathrm{T}}\right),\mathrm{vec}\left({R}_{2}^{\mathrm{T}}\right),\dots ,\mathrm{vec}\left({R}_{m}^{\mathrm{T}}\right)\right)$ then the covariance matrix of $\underline{\stackrel{^}{r}}$ is given by
 $Var(r̲^)=[Y-X(XTGGTX)−1XT]/n,$
where $Y={I}_{m}\otimes \left(\Delta \otimes \Delta \right)$ and $G={I}_{m}\left(G{G}^{\mathrm{T}}\right)$. $\Delta$ is the dispersion matrix $\Sigma$ in correlation form and $G$ a nonsingular $k×k$ matrix such that $G{G}^{\mathrm{T}}={\Delta }^{-1}$ and $G\Delta {G}^{\mathrm{T}}={I}_{k}$. The construction of the matrix $X$ is discussed in Li and McLeod (1981). (Note that the mean, $\mu$, plays no part in calculating $\mathrm{Var}\left(\stackrel{^}{\underline{r}}\right)$ and, therefore, is not required as input to g13dsc.)

## 4References

Li W K and McLeod A I (1981) Distribution of the residual autocorrelations in multivariate ARMA time series models J. Roy. Statist. Soc. Ser. B 43 231–239

## 5Arguments

The output quantities k, n, v, kmax, ip, iq, par, parhld and qq from g13ddc are suitable for input to g13dsc.
1: $\mathbf{k}$Integer Input
On entry: $k$, the number of residual time series.
Constraint: ${\mathbf{k}}\ge 1$.
2: $\mathbf{n}$Integer Input
On entry: $n$, the number of observations in each residual series.
3: $\mathbf{v}\left[{\mathbf{kmax}}×{\mathbf{n}}\right]$const double Input
On entry: ${\mathbf{v}}\left[{\mathbf{kmax}}×\left(\mathit{t}-1\right)+\mathit{i}-1\right]$ must contain an estimate of the $\mathit{i}$th component of ${\epsilon }_{\mathit{t}}$, for $\mathit{i}=1,2,\dots ,k$ and $\mathit{t}=1,2,\dots ,n$.
Constraints:
• no two rows of ${\mathbf{v}}$ may be identical;
• in each row there must be at least two distinct elements.
4: $\mathbf{kmax}$Integer Input
On entry: the first dimension of the arrays V, QQ, R and R0 and the second dimension of the matrix R.
Constraint: ${\mathbf{kmax}}\ge {\mathbf{k}}$.
5: $\mathbf{ip}$Integer Input
On entry: $p$, the number of AR parameter matrices.
Constraint: ${\mathbf{ip}}\ge 0$.
6: $\mathbf{iq}$Integer Input
On entry: $q$, the number of MA parameter matrices.
Constraint: ${\mathbf{iq}}\ge 0$.
Note: ${\mathbf{ip}}={\mathbf{iq}}=0$ is not permitted.
7: $\mathbf{m}$Integer Input
On entry: the value of $m$, the number of residual cross-correlation matrices to be computed. See Section 9.2 for advice on the choice of m.
Constraint: ${\mathbf{ip}}+{\mathbf{iq}}<{\mathbf{m}}<{\mathbf{n}}$.
8: $\mathbf{par}\left[\left({\mathbf{ip}}+{\mathbf{iq}}\right)×{\mathbf{k}}×{\mathbf{k}}\right]$const double Input
Note: the dimension, dim, of the array par must be at least $\left({\mathbf{ip}}+{\mathbf{iq}}\right)×{\mathbf{k}}×{\mathbf{k}}$.
On entry: the parameter estimates read in row by row in the order ${\varphi }_{1},{\varphi }_{2},\dots ,{\varphi }_{p}$, ${\theta }_{1},{\theta }_{2},\dots ,{\theta }_{q}$.
Thus,
• if ${\mathbf{ip}}>0$, ${\mathbf{par}}\left[\left(\mathit{l}-1\right)×k×k+\left(\mathit{i}-1\right)×k+j-1\right]$ must be set equal to an estimate of the $\left(\mathit{i},j\right)$th element of ${\varphi }_{\mathit{l}}$, for $\mathit{l}=1,2,\dots ,p$ and $\mathit{i}=1,2,\dots ,k$;
• if ${\mathbf{iq}}\ge 0$, ${\mathbf{par}}\left[p×k×k+\left(\mathit{l}-1\right)×k×k+\left(\mathit{i}-1\right)×k+j-1\right]$ must be set equal to an estimate of the $\left(\mathit{i},j\right)$th element of ${\theta }_{\mathit{l}}$, for $\mathit{l}=1,2,\dots ,q$ and $\mathit{i}=1,2,\dots ,k$.
The first $p×k×k$ elements of par must satisfy the stationarity condition and the next $q×k×k$ elements of par must satisfy the invertibility condition.
9: $\mathbf{parhld}\left[\left({\mathbf{ip}}+{\mathbf{iq}}\right)×{\mathbf{k}}×{\mathbf{k}}\right]$const Nag_Boolean Input
Note: the dimension, dim, of the array parhld must be at least $\left({\mathbf{ip}}+{\mathbf{iq}}\right)×{\mathbf{k}}×{\mathbf{k}}$.
On entry: ${\mathbf{parhld}}\left[\mathit{i}-1\right]$ must be set to Nag_TRUE if ${\mathbf{par}}\left[\mathit{i}-1\right]$ has been held constant at a pre-specified value and Nag_FALSE if ${\mathbf{par}}\left[\mathit{i}-1\right]$ is a free parameter, for $\mathit{i}=1,2,\dots ,\left(p+q\right)×k×k$.
10: $\mathbf{qq}\left[{\mathbf{kmax}}×{\mathbf{k}}\right]$double Input/Output
On entry: ${\mathbf{qq}}\left[{\mathbf{kmax}}×\left(j-1\right)+i-1\right]$ is an efficient estimate of the $\left(i,j\right)$th element of $\Sigma$. The lower triangle only is needed.
Constraint: ${\mathbf{qq}}$ must be positive definite.
On exit: if ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_G13D_AR, NE_G13D_DIAG, NE_G13D_FACT, NE_G13D_ITER, NE_G13D_MA, NE_G13D_RES, NE_G13D_ZERO_VAR or NE_NOT_POS_DEF, then the upper triangle is set equal to the lower triangle.
11: $\mathbf{ishow}$Integer Input
On entry: must be nonzero if the residual cross-correlation matrices $\left\{{\stackrel{^}{r}}_{ij}\left(l\right)\right\}$ and their standard errors $\left\{\mathrm{se}\left({\stackrel{^}{r}}_{ij}\left(l\right)\right)\right\}$, the modified portmanteau statistic with its significance and a summary table are to be printed. The summary table indicates which elements of the residual correlation matrices are significant at the $5%$ level in either a positive or negative direction; i.e., if ${\stackrel{^}{r}}_{ij}\left(l\right)>1.96×\mathrm{se}\left({\stackrel{^}{r}}_{ij}\left(l\right)\right)$ then a ‘$+$’ is printed, if ${\stackrel{^}{r}}_{ij}\left(l\right)<-1.96×\mathrm{se}\left({\stackrel{^}{r}}_{ij}\left(l\right)\right)$ then a ‘$-$’ is printed, otherwise a fullstop (.) is printed. The summary table is only printed if $k\le 6$ on entry.
The residual cross-correlation matrices, their standard errors and the modified portmanteau statistic with its significance are available also as output variables in r, rcm, chi, idf and siglev.
12: $\mathbf{outfile}$const char * Input
On entry: the name of a file to which diagnostic output will be directed. If outfile is NULL the diagnostic output will be directed to standard output.
13: $\mathbf{r0}\left[{\mathbf{kmax}}×{\mathbf{k}}\right]$double Output
On exit: if $i\ne j$, then ${\mathbf{r0}}\left[{\mathbf{kmax}}×\left(j-1\right)+i-1\right]$ contains an estimate of the $\left(i,j\right)$th element of the residual cross-correlation matrix at lag zero, ${\stackrel{^}{R}}_{0}$. When $i=j$, ${\mathbf{r0}}\left[{\mathbf{kmax}}×\left(j-1\right)+i-1\right]$ contains the standard deviation of the $i$th residual series. If ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_G13D_RES or NE_G13D_ZERO_VAR on exit then the first k rows and columns of r0 are set to zero.
14: $\mathbf{r}\left[\mathit{dim}\right]$double Output
Note: the dimension, dim, of the array r must be at least ${\mathbf{kmax}}×{\mathbf{kmax}}×{\mathbf{m}}$.
where ${\mathbf{R}}\left(l,i,j\right)$ appears in this document, it refers to the array element ${\mathbf{r}}\left[\left(j-1\right)×{\mathbf{kmax}}×{\mathbf{kmax}}+\left(i-1\right)×{\mathbf{kmax}}+l-1\right]$.
On exit: ${\mathbf{R}}\left(\mathit{l},\mathit{i},\mathit{j}\right)$ is an estimate of the $\left(\mathit{i},\mathit{j}\right)$th element of the residual cross-correlation matrix at lag $\mathit{l}$, for $\mathit{i}=1,2,\dots ,k$, $\mathit{j}=1,2,\dots ,k$ and $\mathit{l}=1,2,\dots ,m$. If ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_G13D_RES or NE_G13D_ZERO_VAR on exit then all elements of r are set to zero.
15: $\mathbf{rcm}\left[{\mathbf{pdrcm}}×{\mathbf{m}}×{\mathbf{k}}×{\mathbf{k}}\right]$double Output
Note: the dimension, dim, of the array rcm must be at least ${\mathbf{pdrcm}}×{\mathbf{m}}×{\mathbf{k}}×{\mathbf{k}}$.
On exit: the estimated standard errors and correlations of the elements in the array r. The correlation between ${\mathbf{R}}\left(l,i,j\right)$ and ${\mathbf{R}}\left({l}_{2},{i}_{2},{j}_{2}\right)$ is returned as ${\mathbf{rcm}}\left[{\mathbf{pdrcm}}×t+s\right]$ where $s=\left(l-1\right)×k×k+\left(j-1\right)×k+i$ and $t=\left({l}_{2}-1\right)×k×k+\left({j}_{2}-1\right)×k+{i}_{2}$ except that if $s=t$, then ${\mathbf{rcm}}\left[{\mathbf{pdrcm}}×t+s\right]$ contains the standard error of ${\mathbf{R}}\left(l,i,j\right)$. If on exit, ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_G13D_DIAG or NE_G13D_FACT, then all off-diagonal elements of RCM are set to zero and all diagonal elements are set to $1/\sqrt{n}$.
16: $\mathbf{pdrcm}$Integer Input
On entry: the first dimension of the array RCM.
Constraint: ${\mathbf{pdrcm}}\ge {\mathbf{m}}×{\mathbf{k}}×{\mathbf{k}}$.
17: $\mathbf{chi}$double * Output
On exit: the value of the modified portmanteau statistic, ${Q}_{\left(m\right)}^{*}$. If ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_G13D_RES or NE_G13D_ZERO_VAR on exit then chi is returned as zero.
18: $\mathbf{idf}$Integer * Output
On exit: the number of degrees of freedom of chi.
19: $\mathbf{siglev}$double * Output
On exit: the significance level of chi based on idf degrees of freedom. If ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_G13D_RES or NE_G13D_ZERO_VAR on exit, siglev is returned as one.
20: $\mathbf{fail}$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_G13D_AR
On entry, the AR parameter matrices are outside the stationarity region. To proceed you must supply different parameter estimates in the arrays par and qq.
NE_G13D_ARMA
On entry, ${\mathbf{ip}}=0$ and ${\mathbf{iq}}=0$.
Constraint: ${\mathbf{ip}}={\mathbf{iq}}=0$ must not hold.
NE_G13D_DIAG
The matrix rcm could not be computed because one of its diagonal elements was found to be non-positive. In this case, the off-diagonal elements of rcm are returned as zero and the diagonal elements set to $1/\sqrt{n}$.
NE_G13D_FACT
On entry, the AR operator has a factor in common with the MA operator. To proceed you must either supply different parameter estimates in the array qq or delete this common factor from the model. In this case, the off-diagonal elements of rcm are returned as zero and the diagonal elements set to $1/\sqrt{n}$. All other output quantities will be correct.
NE_G13D_ITER
Excessive iterations needed to find zeros of determinental polynomials.
NE_G13D_MA
On entry, the MA parameter matrices are outside the invertibility region. To proceed you must supply different parameter estimates in the arrays par and qq.
NE_G13D_RES
On entry, at least two of the residual series are identical. In this case chi is set to zero, siglev to one and all the elements of r0 and r are set to zero.
NE_G13D_ZERO_VAR
On entry, at least one of the residual series in the array v has near-zero variance. In this case chi is set to zero, siglev to one and all the elements of r0 and r are set to zero.
NE_INT
On entry, ${\mathbf{ip}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{ip}}\ge 0$.
On entry, ${\mathbf{iq}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{iq}}\ge 0$.
On entry, ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{k}}\ge 1$.
NE_INT_2
On entry, ${\mathbf{kmax}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{kmax}}\ge {\mathbf{k}}$.
On entry, ${\mathbf{m}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{m}}<{\mathbf{n}}$.
NE_INT_3
On entry, ${\mathbf{m}}=⟨\mathit{\text{value}}⟩$, ${\mathbf{ip}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{iq}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{m}}>{\mathbf{ip}}+{\mathbf{iq}}$.
On entry, ${\mathbf{pdrcm}}=⟨\mathit{\text{value}}⟩$, ${\mathbf{m}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{k}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdrcm}}\ge {\mathbf{m}}×{\mathbf{k}}×{\mathbf{k}}$.
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_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_NOT_CLOSE_FILE
Cannot close file $⟨\mathit{\text{value}}⟩$.
NE_NOT_POS_DEF
On entry, the covariance matrix qq is not positive definite. To proceed you must supply different parameter estimates in the arrays par and qq.
NE_NOT_WRITE_FILE
Cannot open file $⟨\mathit{\text{value}}⟩$ for writing.

## 7Accuracy

The computations are believed to be stable.

## 8Parallelism and Performance

g13dsc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
g13dsc 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.

### 9.1Timing

The time taken by g13dsc depends upon the number of residual cross-correlation matrices to be computed, $m$, and the number of time series, $k$.

### 9.2Choice of $\mathbit{m}$

The number of residual cross-correlation matrices to be computed, $m$, should be chosen to ensure that when the ARMA model (1) is written as either an infinite order autoregressive process, i.e.,
 $Wt-μ=∑j=1∞πj(Wt-j-μ)+εt$
or as an infinite order moving average process, i.e.,
 $Wt-μ=∑j= 1∞ψjεt-j+εt$
then the two sequences of $k×k$ matrices $\left\{{\pi }_{1},{\pi }_{2},\dots \right\}$ and $\left\{{\psi }_{1},{\psi }_{2},\dots \right\}$ are such that ${\pi }_{j}$ and ${\psi }_{j}$ are approximately zero for $j>m$. An overestimate of $m$ is, therefore, preferable to an under-estimate of $m$. In many instances the choice $m=10$ will suffice. In practice, to be on the safe side, you should try setting $m=20$.

### 9.3Checking a ‘White Noise’ Model

If you have fitted the ‘white noise’ model
 $Wt-μ=εt$
then g13dsc should be entered with $p=1$, $q=0$, and the first ${k}^{2}$ elements of par and parhld set to zero and Nag_TRUE respectively.

### 9.4Approximate Standard Errors

When ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_G13D_DIAG or NE_G13D_FACT all the standard errors in rcm are set to $1/\sqrt{n}$. This is the asymptotic standard error of ${\stackrel{^}{r}}_{ij}\left(l\right)$ when all the autoregressive and moving average parameters are assumed to be known rather than estimated.

### 9.5Alternative Tests

${\stackrel{^}{R}}_{0}$ is useful in testing for instantaneous causality. If you wish to carry out a likelihood ratio test then the covariance matrix at lag zero $\left({\stackrel{^}{C}}_{0}\right)$ can be used. It can be recovered from ${\stackrel{^}{R}}_{0}$ by setting
 $C^0(i,j) =R^0(i,j)×R^0(i,i)×R^0(j,j), for ​i≠j =R^0(i,j)×R^0(i,j), for ​i=j$

## 10Example

This example fits a bivariate AR(1) model to two series each of length $48$. $\mu$ has been estimated but ${\varphi }_{1}\left(2,1\right)$ has been constrained to be zero. Ten residual cross-correlation matrices are to be computed.

### 10.1Program Text

Program Text (g13dsce.c)

### 10.2Program Data

Program Data (g13dsce.d)

### 10.3Program Results

Program Results (g13dsce.r)