wrap.SMC

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

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Xialu Liu

Nonlinear Time Series Analysis

Ruey Tsay

Rong Chen

wrap.SMC function

<dl><dt>par.natural</dt>
<dd>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.</dd>
<dt>yy</dt>
<dd>the data.</dd>
<dt>mm</dt>
<dd>the Monte Carlo sample size.</dd>
<dt>setseed</dt>
<dd>the seed number.</dd>
<dt>resample</dt>
<dd>the logical value indicating for resampling.</dd></dl>

Arguments

Sequential Monte Carlo Using Sequential Importance Sampling for Stochastic Volatility Models — wrap.SMC

<dl>

<dt>par.natural</dt>
<dd>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.</dd>


<dt>yy</dt>
<dd>the data.</dd>


<dt>mm</dt>
<dd>the Monte Carlo sample size.</dd>


<dt>setseed</dt>
<dd>the seed number.</dd>


<dt>resample</dt>
<dd>the logical value indicating for resampling.</dd>

</dl>

wrap.SMC: Sequential Monte Carlo Using Sequential Importance Sampling for Stochastic Volatility Models

Description

Usage

Value

Arguments

References