| backtest {MTS} | R Documentation |
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.