estimate: Estimates an GARCH model given a specification object using...

In tsgarch: Univariate GARCH Models

Estimates an GARCH model given a specification object using maximum likelihood and autodiff

Description

Estimates an GARCH model given a specification object using maximum likelihood and autodiff

Usage

## S3 method for class 'tsgarch.spec'
estimate(
  object,
  solver = "nloptr",
  control = NULL,
  stationarity_constraint = 0.999,
  keep_tmb = FALSE,
  ...
)

## S3 method for class 'tsgarch.multispec'
estimate(
  object,
  solver = "nloptr",
  control = NULL,
  stationarity_constraint = 0.999,
  keep_tmb = FALSE,
  ...
)

Arguments

object	an object of class “tsgarch.spec” or “tsgarch.multispec”
solver	only “nloptr” is currently supported (see nloptr).
control	solver control parameters.
stationarity_constraint	the bound on the inequality constraint for ensuring the stationary of the GARCH process (see details).
keep_tmb	whether to save and return the TMB object. This is can be used for example when passing different parameters to the model to extract a new output set given the same dataset (used in “tsmarch” package for calculating partitioned standard errors). The downside is that it can increase the size of the returned object by a factor of 8.
...	not currently used.

Details

The underlying code is written using the TMB framework which uses automatic differentiation and hence allows the generation of analytic derivatives.

Stationarity is usually based on the condition that the persistence of the model is less than 1. The argument “stationarity_constraint” allows to fine tune this. For example, setting it to a very high value will effectively render this constraint inactive. The default of 0.999 has been found to be a reasonable bound since values close to one may lead to problems.

Since the nloptr solver make use of analytic Jacobians for the inequality constraint, these are either provided in closed form or calculated as part of the automatic differentiation algorithms implemented in the package.

The estimation makes 2 passes to the solver. The first pass uses no parameter scaling, whilst in the second pass the parameters (as well as bounds) are scaled making use of the estimated hessian from the first pass in order to generate a hopefully more robust solution.

For the multiple specification estimation, parallel functionality is available through the future_lapply function. The user needs to setup a plan in order to make use of this.

Value

An object of class “tsgarch.estimate” or “tsgarch.multi_estimate”.

Author(s)

Alexios Galanos

tsgarch documentation built on Oct. 12, 2024, 1:07 a.m.