cvm_2series

This function computes the Cramer-von Mises statistics between x(t) and y(t-l), for l=-lag,.., lag, and also the combinations of the p-values 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

cvm_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 dependogram for all possible lags.</dd></dl>

Arguments

Cramer-von Mises Moebius statistics for testing independence between the innovations of 2 series of same length — cvm_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 dependogram for all possible lags.</dd>

</dl>

cvm_2series: Cramer-von Mises Moebius statistics for testing independence between the innovations of 2 series of same length

Description

Usage

Value

Arguments

References

Examples