MSM.fit

Fit autoregressive Markov switching models to a univariate time series using the package MSwM.

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

MSM.fit function

<dl><dt>y</dt>
<dd>a time series.</dd>
<dt>p</dt>
<dd>AR order.</dd>
<dt>nregime</dt>
<dd>the number of regimes.</dd>
<dt>include.mean</dt>
<dd>a logical value for including constant terms.</dd>
<dt>sw</dt>
<dd>logical values for whether coefficients are switching. The length of <code>sw</code> has to be equal to the number of coefficients in the model plus include.mean.</dd></dl>

Arguments

Fitting Univariate Autoregressive Markov Switching Models — MSM.fit

<dl>

<dt>y</dt>
<dd>a time series.</dd>


<dt>p</dt>
<dd>AR order.</dd>


<dt>nregime</dt>
<dd>the number of regimes.</dd>


<dt>include.mean</dt>
<dd>a logical value for including constant terms.</dd>


<dt>sw</dt>
<dd>logical values for whether coefficients are switching. The length of <code>sw</code> has to be equal to the number of coefficients in the model plus include.mean.</dd>

</dl>

MSM.fit: Fitting Univariate Autoregressive Markov Switching Models

Description

Usage

Value

Arguments