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VAR

Estimates a Vector Autoregressive model of order p.


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

Examples

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

LPTime

LP Nonparametric Approach to Non-Gaussian Non-Linear Time Series Modelling

v1.0-2
GPL (>= 2)
Authors
Subhadeep Mukhopadhyay, Shinjini Nandi
Initial release
2015-03-03

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