tsfilter.tsmarch: Model Filtering

In tsmarch: Multivariate ARCH Models

Model Filtering

Description

Filters new data based on an already estimated model.

Usage

## S3 method for class 'cgarch.estimate'
tsfilter(
  object,
  y = NULL,
  newxreg = NULL,
  update = TRUE,
  cond_mean = NULL,
  ...
)

## S3 method for class 'dcc.estimate'
tsfilter(
  object,
  y = NULL,
  newxreg = NULL,
  update = TRUE,
  cond_mean = NULL,
  ...
)

## S3 method for class 'gogarch.estimate'
tsfilter(object, y = NULL, newxreg = NULL, cond_mean = NULL, ...)

Arguments

object	an object of class “cgarch.estimate” or “dcc.estimate”.
y	an xts matrix of new values to filter.
newxreg	not used in these models.
update	whether to update certain values using the most recent information less than the new data (see details).
cond_mean	an optional matrix of the filtered conditional mean values.
...	additional arguments for future expansion.

Details

The method filters new data and updates the object with this new information so that it can be called recursively as new data arrives. The “update” argument allows values such as the intercept matrices and transformation estimates (for the “spd” and “empirical” methods) in the dynamic case, and the constant correlation in the constant case, to use information up to and include time T, where T is the time stamp just preceding the new y timestamps. In this way, the filter method can be called recursively and the user can selectively choose to either use the updating scheme or use the original estimated values. Whatever the case, this ensures that there is no look-ahead bias when filtering new information.

Value

A “cgarch.estimate” or “dcc.estimate” object with updated information. All values in the object are updated with the exception of the hessian and scores which remain at their estimation set values.

tsmarch documentation built on April 3, 2025, 7:40 p.m.