Description Usage Arguments Details Value Note References See Also

These functions are S3 methods for class `"cdcc"`

or `"summary.cdcc"`

.

1 2 3 4 5 |

`object` |
an object of class |

`x` |
an object of class |

`digits` |
a number of digits to display. |

`...` |
optional arguments. Currently not in use. |

The function `summary.cdcc()`

computes and returns a list of summary
statistics of the fitted model in `object`

of the `"cdcc"`

class.

Some of the values are printed up to certain decimal places. Actual values of
individual components are displayed separately, for instance, by
`summary(object)$coefficients`

. See the **Value** section for a list of components.

For residual diagnostics, `residDiag`

carries out the Jarque-Bera test of
normality for the standardized residuals and the Ljung-Box test for serial correlations
in the standardized and squared standardized residuals.

In addition to those available in the `object`

of the `"cdcc"`

class,
the following list components are added.

`nobs` |
the number of observations. |

`mu` |
the estimate of the conditional mean. |

`garch.par` |
the estimates of the parameters in the GARCH part. |

`cDcc.par` |
the estimates of the parameters in the cDCC part. |

`coef` |
the table of all estimates with robust s.e. and associated inferencial statistics. |

`convergence` |
Integer codes for the convergence status of the 1st and 2nd stage optimization.
See the help of |

`counts` |
a matrix containing the number of calls to the objective function and its (numerical)
gradient during the 1st and 2nd stage optimization. The number of calls to the gradient
in the 2nd stage is "NA" because it does not use it. See the help of |

`logLik` |
a value of the lig-likelihood function at the estimates. |

`AIC` |
Akaike's information criterion. |

`BIC` |
Bayesian information criterion. |

`CAIC` |
Consistent Akaike's information criterion. |

It is time consuming to implement the summary method for ‘`cdcc`

’ class
object. This is because obtaining numerical derivatives, which are used for computing
the (robust) standard errors of the parameter estimates, is computationally demanding.

Aielli, G.P. (2013),
“Dynamic Conditional Correlation: On Properties and Estimation.”
*Journal of Business and Economic Statistics*
**31**, 282–299.

Engle, R.F. and K. Sheppard (2001),
“Theoretical and Empirical Properties of Dynamic
Conditional Correlation Multivariate GARCH.”
*Stern Finance Working Paper Series*
FIN-01-027 (Revised in Dec. 2001),
New York University Stern School of Business.

Engle, R.F. (2002),
“Dynamic Conditional Correlation: A Simple Class of
Multivariate Generalized Autoregressive Conditional
Heteroskedasticity Models.”
*Journal of Business and Economic Statistics*
**20**, 339–350.

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