backtest: Backtesting of a scalar ARIMA model

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Backtesting of a scalar ARIMA model

Description

Perform out-of-sample prediction of a given ARIMA model and compute the summary statistics

Usage

backtest(m1, rt, orig, h = 1, xre = NULL, fixed = NULL, 
  inc.mean = TRUE, reest = 1, method = c("CSS-ML"))

Arguments

m1	An output of the arima command for scalar time series
rt	The time series under consideration
orig	The starting forecast origin. It should be less than the length of the underlying time series
h	The forecast horizon. For a given h, it computes 1-step to h-step ahead forecasts
inc.mean	A logical switch. It is true if mean vector is estimated.
fixed	A vector of the length of the number of coefficients of the ARIMA model. It is used in R for parameter constraint.
xre	A matrix containing the exogeneous variables used in the ARIMA model
reest	A control variable used to re-fit the model in prediction. The program will re-estimate the model for every new reest observations. The default is 1. That is, re-estimate the model with every new data point.
method	Estimation method in the ARIMA model

Details

Perform estimation-prediction-reestimation in the forecasting subsample, and to compuate the summary statistics

Value

origion	Forecast origin
error	forecast errors
forecasts	forecasts
rmse	Root mean squared forecast errors
mabso	Mean absolute forecast errors
reest	Return the reest value

Author(s)

Ruey S. Tsay

References

Tsay (2010). Analysis of Financial Time Series, 3rd. John Wiley. Hoboken, NJ.

MTS documentation built on April 11, 2022, 5:07 p.m.