computeGradient

Computes the gradient under the null hypothesis

internal

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).

Matthieu Stigler

tsDyn

Nonlinear Time Series Models with Regime Switching

Antonio Fabio Di Narzo

Jose Luis Aznarte

computeGradient function

<dl><dt>object</dt>
<dd>fitted star model</dd>
<dt>...</dt>
<dd>currently unused</dd></dl>

Arguments

Author

computeGradient — computeGradient

<dl>

<dt>object</dt>
<dd>fitted star model</dd>


<dt>...</dt>
<dd>currently unused</dd>

</dl>

computeGradient: computeGradient

Description

Usage

Value

Arguments

Author

References

See Also

Examples