https://github.com/cran/ltsa
Tip revision: 7e0165a782fedb4fc5aaa42677ea20ad995930a3 authored by A.I. McLeod on 30 November 2010, 19:46:10 UTC
version 1.4
version 1.4
Tip revision: 7e0165a
DLSim.c
/**************************************************************************************
DLSim
input parameters:
error - vector of length n, innovation sequence
nR - length of time series
c - vector of length n containing the autocovariances
EPSL - machine epsilon in double precision
output parameters:
z - vector of length n, simulated time series
ifault - 0, OK ; 1, c is not p.d.
***************************************************************************************/
#include "nrutil.h"
#include <math.h>
void DLSim(double *z, double *error, int *nR, double *c, double *EPSL, int *fault)
{
double sum;
int i,j,k,n;
double EPS;
VECTOR v,phi,phiki,phikj;
*fault = 0;
n = *nR;
EPS = *EPSL;
if (n < 1) *fault = 1;
v = Vector(n);
phi = Vector(n);
phiki = Vector(n);
phikj = Vector(n);
v[0] = c[0];
z[0] = error[0]*sqrt(v[0]);
if (c[0] <= EPS) *fault = 1;
phi[0] = c[1] / c[0];
phiki[0] = phi[0];
v[1] = v[0] * (1.0 - phi[0]*phi[0]);
z[1] = sqrt(v[1])*error[1] + phi[0] * z[0];
if (v[1] <= EPS) *fault = 1;
for (k = 2; k < n; k++)
{
sum = 0.0;
for (i = 1; i < k; i++)
sum += phiki[i - 1] * c[k - i];
phi[k - 1] = (c[k] - sum) / v[k - 1];
for (j = 1; j< k; j++)
{
phikj[j - 1] = phiki[j - 1] - phi[k - 1] * phiki[k - j - 1];
}
phikj[k - 1] = phi[k - 1];
sum = 0.0;
for (j = 1; j<=k; j++)
{
sum += phikj[j - 1] * z[k - j];
phiki[j - 1] = phikj[j - 1];
}
v[k] = v[k - 1] * (1.0 - phi[k - 1]*phi[k - 1]);
if (v[k] <= EPS) *fault = 1;
error[k] *= sqrt(v[k]);
z[k] = error[k] + sum;
}
free_vector(v);
free_vector(phi);
free_vector(phiki);
free_vector(phikj);
return;
}