# recserar¶

## Purpose¶

Computes a vector of autoregressive recursive series.

## Format¶

y = recserar(x, y0, rho)
Parameters
• x (NxK matrix) – If simulating an AR process, this would contain the error term and constant if included in the model.

• y0 (PxK matrix) – The starting values for the series.

• rho (PxK matrix) – The AR parameters.

Returns

y (NxK matrix) – Contains the series.

## Examples¶

### AR(1) without constant¶

// Starting value for the time series
y0 = 0;

// AR(1) parameter
rho = 0.6;

// Innovations
eps = rndn(10, 1);

// Simulate AR(1) model
y = recserar(eps, y0, rho);


### AR(2) with constant¶

// Starting value for the time series
y0 = { 0, 0 };

// AR(2) parameters
rho = { 0.6, -0.3 };

// Constant term
const = 1.3;

// Innovations
eps = rndn(10, 1);

// Simulate AR(2) model with constant
y = recserar(eps + const, y0, rho);


### Example 3¶

n = 10;

sig = { 1 -.3, -.3 1 };
mu = { 0, 0 };
e = rndMVn(n, mu, sig);

x = ones(n, 1)~rndn(n, 3);

b = 1|2|3|4;

rho = { 0.5, 0.3 };
y0 = zeros(1, 2);
y = recserar(x*b+e, y0, rho);


In this example, two autoregressive series are formed using simulated data. The general form of the series can be written:

y[1,t] = rho[1,1]*y[1,t-1] + x[t,.]*b + e[1,t];
y[2,t] = rho[2,1]*y[2,t-1] + x[t,.]*b + e[2,t];


The error terms ($$e[1,t]$$ and $$e[2,t]$$) are not individually serially correlated, but they are contemporaneously correlated with each other. The variance-covariance matrix is $$\sigma$$.

## Remarks¶

recserar() is particularly useful in dealing with time series.

Typically, the result would be thought of as $$K$$ vectors of length $$N$$.

y0 contains the first $$P$$ values of each of these vectors (thus, these are prespecified). The remaining elements are constructed by computing a $$P^{th}$$ order “autoregressive” recursion, with weights given by a, and then by adding the result to the corresponding elements of x. That is, the $$t^{th}$$ row of y is given by:

y[t,.] = x[t,.] + a[1,.] * y[t-1,.] +...+ a[P,.] * y[t-p,.], t = P + 1,...N


and

y[t,.] = y0[t,.], t = 1,...,P


Note that the first $$P$$ rows of x are not used.