# mAr.sim: Simulation from a multivariate AR(p) model In mAr: Multivariate AutoRegressive analysis

## Description

Simulation from an m-variate AR(p) model

## Usage

 `1` ```mAr.sim(w, A, C, N, ...) ```

## Arguments

 `w` vector of intercept terms `A` matrix of AR coefficients `C` noise covariance matrix `N` length of output time series `...` additional arguments

## Details

Simulation from an m-variate AR(p) model given by

X[t]=w + A1 X[t-1] +...+ Ap X[t-p] +e[t]

where
X[t]=[X1(t)...Xm(t)]' is a vector of length m
w is a m-length vector of intercept terms
A=[A1 ... Ap] is a m x mp matrix of autoregressive coefficients
e(t) is a m-length uncorrelated noise vector with mean 0 and m x m covariance matrix C

## Value

returns a list containg the N simulated observations for each of the m time series

S. M. Barbosa

## References

Neumaier, A. and Schneider, T. (2001), Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Transactions on Mathematical Software, 27, 1, 27-57.

Schneider, T. and Neumaier, A. (2001), A Matlab package fo the estimation of parameters and eigenmodes of multivariate autoregressive models, 27, 1, 58-65.

Lutkepohl, H. (1993), Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin.

## Examples

 ```1 2 3 4``` ```w=c(0.25,0.1) C=rbind(c(1,0.5),c(0.5,1.5)) A=rbind(c(0.4,1.2,0.35,-0.3),c(0.3,0.7,-0.4,-0.5)) x=mAr.sim(w,A,C,N=300) ```

### Example output

```Loading required package: MASS
```

mAr documentation built on May 30, 2017, 6:50 a.m.