VAR: Estimates a Vector Autoregressive model of order p.

In LPTime: LP Nonparametric Approach to Non-Gaussian Non-Linear Time Series Modelling

Description

Estimation of a Vector Autoregressive model (VAR) by computing OLS per equation.

Usage

VAR(y, p = 1, exogen = NULL)

Arguments

y	Endogenous variable for the VAR model.
p	lag-order for the autoregressive model.
exogen	Exogenous variable for the VAR model.

Details

Estimates a VAR by OLS per equation. The model is of the following form

\bold{y}_t = A_1 \bold{y}_{t-1} + … + A_p \bold{y}_{t-p} + CD_t + \bold{u}_t

where \bold{y}_t is a K \times 1 vector of endogenous variables and u_t assigns a spherical disturbance term of the same dimension. The coefficient matrices A_1, …, A_p are of dimension K \times K. No seasonality or trend term can be included in the model.

Value

A matrix of coefficients from fitting the VAR model.

Author(s)

Shinjini Nandi

References

Wei, William W.S. (2006). Time Series Analysis - Univariate and Multivariate Methods

Brockwell, P.J. and Davis, R.A. (1996). Introduction to Time Series and Forecasting , Second Edition, Springer, New York

See Also

LPTime

Examples

library(LPTime)
data(EyeTrack.sample)
head( VAR(y = EyeTrack.sample, p = 2))

LPTime documentation built on May 2, 2019, 7:18 a.m.