文件名称:ar_g
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PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model
imposing stability restrictions using Gibbs sampling
y = b0 + y(t-1) b1 + y(t-2) b2 +,...,y(t-k) bk + E,
E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T),
V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)- PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model
imposing stability restrictions using Gibbs sampling
y = b0+ y(t-1) b1+ y(t-2) b2+,...,y(t-k) bk+ E,
E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T),
V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)
imposing stability restrictions using Gibbs sampling
y = b0 + y(t-1) b1 + y(t-2) b2 +,...,y(t-k) bk + E,
E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T),
V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)- PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model
imposing stability restrictions using Gibbs sampling
y = b0+ y(t-1) b1+ y(t-2) b2+,...,y(t-k) bk+ E,
E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T),
V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)
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ar_g.m