grouped_garch: Fit grouped GARCH model to time series
In MichaelHoerner/groupedtseries: Analysis of grouped time series

Description Usage Arguments Details Value

Fit a Generalized Autoregressive Conditional Heteroscedastic GARCH(p,q) time series model to the data by computing the maximum-likelihood estimates of the conditionally normal model to a group of univariate time series by finding coefficients that serve best the whole group.

grouped_garch(
  x,
  order = c(1, 1),
  series = NULL,
  max.fitting.order = NULL,
  mean_mode = "no_change",
  control = garch.control(...),
  ...
)

`x`	a two-dimensional numeric vector of a grouped time series with accompanying group index in second dimension. One-dimensional if input shall be only a single time series.
`order`	a vector c(p,q) that indicates the order p of ARCH and q of GARCH part
`series`	name for the series. Defaults to `deparse(substitute(x))`
`control`	a list of control parameters as set up by `garch.control`
`...`	additional arguments for `qr` when computing the asymptotic covariance matrix.
`maxiter`	gives the maximum number of log-likelihood function evaluations `maxiter` and the maximum number of iterations `2*maxiter` the optimizer is allowed to compute.
`trace`	logical. Trace optimizer output?
`start`	If given this numeric vector is used as the initial estimate of the GARCH coefficients. Default initialization is to set the GARCH parameters to slightly positive values and to initialize the intercept such that the unconditional variance of the initial GARCH is equal to the variance of `x`
`grad`	character indicating whether analytical gradients or a numerical approximation is used for the optimization.
`abstol`	absolute function convergence tolerance.
`reltol`	relative function convergence tolerance.
`xtol`	coefficient-convergence tolerance.
`falsetol`	false convergence tolerance.

garch uses a Quasi-Newton optimizer to find the maximum likelihood estimates of the conditionally normal model. The first max(p, q) values are assumed to be fixed. The optimizer uses a hessian approximation computed from the BFGS update. Only a Cholesky factor of the Hessian approximation is stored. For more details see Dennis et al. (1981), Dennis and Mei (1979), Dennis and More (1977), and Goldfarb (1976). The gradient is either computed analytically or using a numerical approximation.

Object of class GARCH.

`order`	the order of the fitted model.
`coef`	coef estimated GARCH coefficients for the fitted model across all grouped time series.
`n.likeli`	the negative log-likelihood function evaluated at the coefficient estimates (apart from some constant).
`n.used`	the number of observations of `x`.
`residuals`	the series of residuals.
`fitted.values`	the bivariate series of conditional standard deviation predictions for `x`.
`series`	the name of the series `x`.
`frequency`	the frequency of the series `x`.
`call`	the call of the `garch` function.
`vcov`	outer product of gradient estimate of the asymptotic-theory covariance matrix for the coefficient estimates.