crosscor_2series

This function computes the cross-correlations between x(t) and y(t-l), for l=-lag,.., lag, and also the combination (Wald's type) of these statistics.

Computation of test statistics of independence between (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.

Bruno Remillard

IndGenErrors

Tests of Independence Between Innovations of Generalized Error
Models

Kilani Ghoudi

Pierre Duchesne

crosscor_2series function

<dl><dt>x</dt>
<dd>Pseudo-observations (or residuals) of first series</dd>
<dt>y</dt>
<dd>Pseudo-observations (or residuals) of second series</dd>
<dt>lag</dt>
<dd>Maximum number of lags around 0</dd>
<dt>graph</dt>
<dd>Set to TRUE for a correlogram for all possible lags.</dd></dl>

Arguments

Cross-correlations for testing independence between the innovations of 2 series of same length — crosscor_2series

<dl>

<dt>x</dt>
<dd>Pseudo-observations (or residuals) of first series</dd>


<dt>y</dt>
<dd>Pseudo-observations (or residuals) of second series</dd>


<dt>lag</dt>
<dd>Maximum number of lags around 0</dd>


<dt>graph</dt>
<dd>Set to TRUE for a correlogram for all possible lags.</dd>

</dl>

crosscor_2series: Cross-correlations for testing independence between the innovations of 2 series of same length

Description

Usage

Value

Arguments

References

Examples