Tidy summarizes information about the components of a model.
A model component might be a single term in a regression, a single
hypothesis, a cluster, or a class. Exactly what tidy considers to be a
model component varies cross models but is usually self-evident.
If a model has several distinct types of components, you will need to
specify which components to return.

# S3 method for garch
glance(x, test = c("box-ljung-test", "jarque-bera-test"), ...)

## Arguments

x |
A `garch` object returned by `tseries::garch()` . |

test |
Character specification of which hypothesis test to use. The
`garch` function reports 2 hypothesis tests: Jarque-Bera to residuals
and Box-Ljung to squared residuals. |

... |
Additional arguments. Not used. Needed to match generic
signature only. **Cautionary note:** Misspelled arguments will be
absorbed in `...` , where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass `conf.lvel = 0.9` , all computation will
proceed using `conf.level = 0.95` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## See also

## Value

A `tibble::tibble()`

with exactly one row and columns:

AICAkaike's Information Criterion for the model.

BICBayesian Information Criterion for the model.

logLikThe log-likelihood of the model. [stats::logLik()] may be a useful reference.

methodWhich method was used.

nobsNumber of observations used.

p.valueP-value corresponding to the test statistic.

statisticTest statistic.

parameterParameter field in the htest, typically degrees of
freedom.