This function estimates model parameters and latent log volatilities for stochastic volatility models:
y(t) = eps(t)*exp(h(t)/2), h(t+1) = mu + phi*(h(t)-mu) + eta(t)
eps(t)~i.i.d. N(0,1), eta(t)~i.i.d. N(0,sigma_eta^2)
where we assume the correlation between eps(t) and eta(t) equals to rho.
The highly efficient Markov chain Monte Carlo algorithm is based on the mixture sampler by Omori, Chib, Shephard and Nakajima (2007), but it further corrects the approximation error within the sampling algorithm. See Takahashi, Omori and Watanabe (2022+) for more details.
Omori, Y., Chib, S., Shephard, N., and J. Nakajima (2007), "Stochastic volatility model with leverage: fast and efficient likelihood inference," Journal of Econometrics, 140-2, 425-449.
Takahashi, M., Omori, Y. and T. Watanabe (2022+), Stochastic volatility and realized stochastic volatility models. JSS Research Series in Statistics, in press. Springer, Singapore.
sv_mcmc, asv_mcmc, sv_pf, asv_pf, sv_apf, asv_apf