Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).

Nonlinear Time Series Models with Regime Switching

package-ubuntu-20.04-r-4.0.5#cran#tsDyn-10-1.2

<p>Generic function to compute the Mean Squared Error of a fitted model.
</p>

mse(object, ...)

## Default S3 method:
mse(object, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>object</code></td>
<td>
<p>object of class <code>nlar.fit</code></p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>additional arguments to <code>mse</code></p>
</td>
</tr>
</table>

<p>Computed MSE for the fitted model.
</p>

package-ubuntu-20.04-r-4.0.5#cran#tsDyn-10-1.2#docs#mse

`object`	object of class `nlar.fit`
`...`	additional arguments to `mse`

mse

Description

Usage

Arguments

Value

Author(s)

tsDyn

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