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>Computes the gradient under the null hypothesis
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>object</code></td>
<td>
<p>fitted star model</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>currently unused</p>
</td>
</tr>
</table>

<p><code><a href="addRegime.html">addRegime</a></code>
</p>

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

computeGradient

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

tsDyn

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`object`	fitted star model
`...`	currently unused