delta

delta.test

delta statistic of conditional independence and associated bootstrap test

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

delta function

<dl><dt>x</dt>
<dd>time series</dd>
<dt>m</dt>
<dd>vector of embedding dimensions</dd>
<dt>d</dt>
<dd>time delay</dd>
<dt>eps</dt>
<dd>vector of length scales</dd>
<dt>B</dt>
<dd>number of bootstrap replications</dd></dl>

Arguments

Results are sensible to the choice of the window
<code>eps</code>. So, try the test for a grid of <code>m</code> and <code>eps</code> values.
Also, be aware of the course of dimensionality: m can't be too high for
relatively small time series. See references for further details.

Warning

Author

delta test of conditional independence — delta

<dl>

<dt>x</dt>
<dd>time series</dd>


<dt>m</dt>
<dd>vector of embedding dimensions</dd>


<dt>d</dt>
<dd>time delay</dd>


<dt>eps</dt>
<dd>vector of length scales</dd>


<dt>B</dt>
<dd>number of bootstrap replications</dd>

</dl>

delta: delta test of conditional independence

Description

Usage

Value

Arguments

Warning

Author

Details

References

See Also

Examples