fit: Estimation of the ARIMA model

In tfarima: Transfer Function and ARIMA Models

Estimation of the ARIMA model

Description

fit fits the univariate model to the time series z.

Usage

## S3 method for class 'tfm'
fit(
  mdl,
  y = NULL,
  method = c("exact", "cond"),
  optim.method = "BFGS",
  show.iter = FALSE,
  fit.noise = TRUE,
  envir = NULL,
  ...
)

fit(mdl, ...)

## S3 method for class 'um'
fit(
  mdl,
  z = NULL,
  method = c("exact", "cond"),
  optim.method = "BFGS",
  show.iter = FALSE,
  envir = NULL,
  ...
)

Arguments

mdl	an object of class um or tfm.
y	a ts object.
method	Exact/conditional maximum likelihood.
optim.method	the method argument of the optim function.
show.iter	logical value to show or hide the estimates at the different iterations.
fit.noise	logical. If TRUE parameters of the noise model are fixed.
envir	environment in which the function arguments are evaluated. If NULL the calling environment of this function will be used.
...	additional arguments.
z	a time series.

Value

A tfm object.

An object of class "um" with the estimated parameters.

Note

The um function estimates the corresponding ARIMA model when a time series is provided. The fit function is useful to fit a model to several time series, for example, in a Monte Carlo study.

Examples

z <- AirPassengers
airl <- um(i = list(1, c(1, 12)), ma = list(1, c(1, 12)), bc = TRUE)
airl <- fit(airl, z)

tfarima documentation built on May 20, 2022, 5:06 p.m.