wrap.SMC: Sequential Monte Carlo Using Sequential Importance Sampling...

In NTS: Nonlinear Time Series Analysis

Sequential Monte Carlo Using Sequential Importance Sampling for Stochastic Volatility Models

Description

The function implements the sequential Monte Carlo method using sequential importance sampling for stochastic volatility models.

Usage

wrap.SMC(par.natural, yy, mm, setseed = T, resample = T)

Arguments

par.natural	contains three parameters in AR(1) model. The first one is the stationary mean, the second is the AR coefficient, and the third is stationary variance.
yy	the data.
mm	the Monte Carlo sample size.
setseed	the seed number.
resample	the logical value indicating for resampling.

Value

The function returns the log-likelihood of the data.

References

Tsay, R. and Chen, R. (2018). Nonlinear Time Series Analysis. John Wiley & Sons, New Jersey.

NTS documentation built on Sept. 25, 2023, 1:08 a.m.