/* nag_tsa_uni_garch_asym2_estim (g13fcc) Example Program.
*
* Copyright 2022 Numerical Algorithms Group.
*
* NAG C Library
*
* Mark 28.4, 2022.
*
*/
#include <ctype.h>
#include <math.h>
#include <nag.h>
#include <stdio.h>
#define X(I, J) x[(I)*tdx + (J)]
int main(void) {
/* Integer scalar and array declarations */
Integer exit_status = 0;
Integer i, j, k, npar, tdc, tdx, lstate, lr;
Integer *state = 0;
/* NAG structures and data types */
NagError fail;
Nag_Boolean fcall;
/* Double scalar and array declarations */
double fac1, pht, lgf, xterm;
double *covar = 0, *cvar = 0, *et = 0, *ht = 0, *sc = 0;
double *se = 0, *theta = 0, *x = 0, *yt = 0, *r = 0;
/* Choose the base generator */
Nag_BaseRNG genid = Nag_Basic;
Integer subid = 0;
/* Set the seed */
Integer seed[] = {111};
Integer lseed = 1;
/* Set parameters for the (randomly generated) time series ... */
/* Generate data assuming normally distributed errors */
Nag_ErrorDistn dist = Nag_NormalDistn;
double df = 0;
/* Size of the time series */
Integer num = 1500;
/* MA and AR parameters */
Integer ip = 1;
Integer iq = 1;
double param[] = {0.08, 0.2, 0.7};
/* Asymmetry parameter */
double gamma = -0.4;
/* Regression parameters */
Integer nreg = 2;
double mean = 3.0;
double bx[] = {1.5, 2.5};
/* ... end of parameters for (randomly generated) time series */
/* When fitting a model to the time series ... */
/* Include mean in the model */
Integer mn = 1;
/* Use the following maximum number of iterations and tolerance */
Integer maxit = 50;
double tol = 1e-12;
/* Enforce stationary conditions */
Nag_Garch_Stationary_Type stat_opt = Nag_Garch_Stationary_True;
/* Estimate initial values for regression parameters */
Nag_Garch_Est_Initial_Type est_opt = Nag_Garch_Est_Initial_True;
/* Set the number of values to forecast from the fitted model */
Integer nt = 3;
/* ... end of model fitting options */
/* Initialize the error structure */
INIT_FAIL(fail);
printf("nag_tsa_uni_garch_asym2_estim (g13fcc) Example Program Results\n\n");
/* Get the length of the state array */
lstate = -1;
nag_rand_init_repeat(genid, subid, seed, lseed, state, &lstate, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_init_repeat (g05kfc).\n%s\n", fail.message);
exit_status = 1;
goto END;
}
/* Derive various amounts */
npar = iq + ip + 1;
tdc = npar + mn + nreg + 1;
tdx = nreg;
/* Calculate the size of the reference vector */
lr = 2 * (iq + ip + 2);
/* Allocate arrays */
if (!(covar = NAG_ALLOC((npar + mn + nreg + 1) * tdc, double)) ||
!(et = NAG_ALLOC(num, double)) || !(ht = NAG_ALLOC(num, double)) ||
!(sc = NAG_ALLOC(npar + mn + nreg + 1, double)) ||
!(se = NAG_ALLOC(npar + mn + nreg + 1, double)) ||
!(state = NAG_ALLOC(lstate, Integer)) || !(r = NAG_ALLOC(lr, double)) ||
!(theta = NAG_ALLOC(npar + mn + nreg + 1, double)) ||
!(x = NAG_ALLOC(num * tdx, double)) || !(cvar = NAG_ALLOC(nt, double)) ||
!(yt = NAG_ALLOC(num, double))) {
printf("Allocation failure\n");
exit_status = -1;
goto END;
}
/* Initialize the generator to a repeatable sequence */
nag_rand_init_repeat(genid, subid, seed, lseed, state, &lstate, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_init_repeat (g05kfc).\n%s\n", fail.message);
exit_status = 1;
goto END;
}
/* Set up the time dependent exogenous matrix x */
for (i = 0; i < num; ++i) {
fac1 = (double)(i + 1) * .01;
X(i, 0) = sin(fac1) * 0.7 + 0.01;
X(i, 1) = fac1 * 0.1 + 0.5;
}
/* Generate a realization of a random AGARCH II time series to use */
fcall = Nag_TRUE;
nag_rand_times_garch_asym2(dist, num, ip, iq, param, gamma, df, ht, yt, fcall,
r, lr, state, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_times_garch_asym2 (g05pec).\n%s\n",
fail.message);
exit_status = 1;
goto END;
}
/* Adjust the randomly generated time series to take into account for the
exogenous matrix x */
for (i = 0; i < num; ++i) {
xterm = 0.0;
for (k = 0; k < nreg; ++k)
xterm += X(i, k) * bx[k];
if (mn == 1)
yt[i] = mean + xterm + yt[i];
else
yt[i] = xterm + yt[i];
}
/* Set initial estimates for the parameters */
for (i = 0; i < npar; ++i) {
theta[i] = param[i] * 0.5;
}
theta[npar] = gamma * 0.5;
if (mn == 1) {
theta[npar + mn] = mean * 0.5;
}
if (est_opt != Nag_Garch_Est_Initial_True) {
for (i = 0; i < nreg; ++i)
theta[npar + mn + 1 + i] = bx[i] * 0.5;
}
/* nag_tsa_uni_garch_asym2_estim (g13fcc).
* Univariate time series, parameter estimation for a GARCH
* process with asymmetry of the form
* (|epsilon_(t-1)| + gamma epsilon_(t-1))^2
*/
nag_tsa_uni_garch_asym2_estim(yt, x, tdx, num, ip, iq, nreg, mn, theta, se,
sc, covar, tdc, &pht, et, ht, &lgf, stat_opt,
est_opt, maxit, tol, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_tsa_uni_garch_asym2_estim (g13fcc).\n%s\n",
fail.message);
exit_status = 1;
goto END;
}
/* Display the results */
printf(" Parameter estimates Standard errors "
"Correct values\n");
for (j = 0; j < npar; ++j)
printf("%20.4f (%6.4f) %20.4f\n", theta[j], se[j], param[j]);
printf("%20.4f (%6.4f) %20.4f\n", theta[npar], se[npar], gamma);
if (mn == 1)
printf("%20.4f (%6.4f) %20.4f\n", theta[npar + mn],
se[npar + mn], mean);
for (j = 0; j < nreg; ++j)
printf("%20.4f (%6.4f) %20.4f\n", theta[mn + npar + 1 + j],
se[mn + npar + 1 + j], bx[j]);
/* Now forecast nt steps ahead */
gamma = theta[npar];
/* nag_tsa_uni_garch_asym2_forecast (g13fdc).
* Univariate time series, forecast function for a GARCH
* process with asymmetry of the form
* (|epsilon_(t-1)| + gamma epsilon_(t-1))^2
*/
nag_tsa_uni_garch_asym2_forecast(num, nt, ip, iq, theta, gamma, cvar, ht, et,
&fail);
printf("\n%" NAG_IFMT " step forecast = %8.4f\n", nt, cvar[nt - 1]);
END:
NAG_FREE(covar);
NAG_FREE(et);
NAG_FREE(ht);
NAG_FREE(state);
NAG_FREE(r);
NAG_FREE(sc);
NAG_FREE(se);
NAG_FREE(theta);
NAG_FREE(cvar);
NAG_FREE(x);
NAG_FREE(yt);
return exit_status;
}