forecasting: Forecasting of a multivariate TAR model.

In mtarm: Bayesian Estimation of Multivariate Threshold Autoregressive Models

Forecasting of a multivariate TAR model.

Description

This function computes forecasting from a fitted multivariate TAR model.

Usage

forecasting(
  object,
  data,
  out.of.sample = FALSE,
  credible = 0.95,
  row.names,
  setar = NULL
)

Arguments

object	an object of the class mtar.
data	an (optional) data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the future values of the threshold series as well as the exogenous series in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which mtar is called.
out.of.sample	an (optional) logical variable. If TRUE, then the log-score is computed, which is a measurement to assess density forecasts. Therefore, the data.frame specified in the argument data must to include the true values of the output series.
credible	an (optional) value for the level of the credible intervals. By default, credible is set to 0.95.
row.names	an vector that allows the user to name the time point to which each row in the data set data corresponds.
setar	an (optional) positive integer indicating the component of the output series which is the threshold variable. By default,setar is set to NULL, which indicates that the fitted model is not a SETAR.

Value

a list with the following component

ypred	a matrix with the results of the forecasting,
summary	a matrix with the mean and credible intervals of the forecasting,

References

Nieto, F.H. (2005) Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data. Communications in Statistics - Theory and Methods, 34, 905-930.

Romero, L.V. and Calderon, S.A. (2021) Bayesian estimation of a multivariate TAR model when the noise process follows a Student-t distribution. Communications in Statistics - Theory and Methods, 50, 2508-2530.

Calderon, S.A. and Nieto, F.H. (2017) Bayesian analysis of multivariate threshold autoregressive models with missing data. Communications in Statistics - Theory and Methods, 46, 296-318.

Karlsson, S. (2013) Chapter 15-Forecasting with Bayesian Vector Autoregression. In Elliott, G. and Timmermann, A. Handbook of Economic Forecasting, Volume 2, 791–89, Elsevier.

Examples

###### Example 1: Returns of the closing prices of three financial indexes
data(returns)
fit1 <- mtar(~ COLCAP + BOVESPA | SP500, data=returns, row.names=Date,
             dist="Gaussian", ars=list(p=c(1,1,2)), n.burnin=100,
             n.sim=3000, n.thin=2)
out1 <- forecasting(fit1,data=subset(returns,Date >= "2016-03-20"),row.names=Date)
out1$summary

###### Example 2: Rainfall and two river flows in Colombia
data(riverflows)
fit2 <- mtar(~ Bedon + LaPlata | Rainfall, data=riverflows, row.names=Date,
             dist="Gaussian", ars=list(p=c(5,5,5)), n.burnin=2000,
             n.sim=3000, n.thin=2)
out2 <- forecasting(fit2,data=subset(riverflows,Date >= "2009-04-09"),row.names=Date)
out2$summary

mtarm documentation built on June 8, 2025, 10:20 a.m.