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"), ...)



A garch object returned by tseries::garch().


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

glance(), tseries::garch(), []

Other garch tidiers: tidy.garch()


A tibble::tibble() with exactly one row and columns:


Akaike's Information Criterion for the model.


Bayesian Information Criterion for the model.


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


Which method was used.


Number of observations used.


P-value corresponding to the test statistic.


Test statistic.


Parameter field in the htest, typically degrees of freedom.