backtest

An output of the arima command for scalar time series

The time series under consideration

The starting forecast origin. It should be less than the length of the underlying time series

orig

The forecast horizon. For a given h, it computes 1-step to h-step ahead forecasts

A logical switch. It is true if mean vector is estimated.

inc.mean

A vector of the length of the number of coefficients of the ARIMA model. It is used 
in R for parameter constraint.

fixed

A matrix containing the exogeneous variables used in the ARIMA model

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.

reest

Estimation method in the ARIMA model

method

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

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

backtest: Backtesting of a scalar ARIMA model

Description

Usage

Arguments

Value

Details

References