arimaSS

Purpose

Estimates ARIMA models using a state space representation, the Kalman filter, and maximum likelihood.

Format

vOut = arimaSS(y, p, d, q, trend, const)
Parameters
  • y (Nx1 vector) – data.

  • p (Scalar) – the autoregressive order.

  • d (Scalar) – the order of differencing.

  • q (Scalar) – the moving average order.

  • trend (Scalar) – an indicator variable to include a trend in the model. Set to 1 to include trend, 0 otherwise.

  • const (Scalar) – an indicator variable to include a constant in the model. Set to 1 to include trend, 0 otherwise.

Returns

vOut (struct) –

An instance of an arimamtOut structure containing the following members:

amo.aic

Scalar, value of the Akaike information criterion.

amo.b

Kx1 vector, estimated model coefficients.

amo.e

Nx1 vector, residual from fitted model.

amo.ll

Scalar, the value of the log likelihood function.

amo.sbc

Scalar, value of the Schwartz Bayesian criterion.

amo.lrs

Lx1 vector, the Likelihood Ratio Statistic.

amo.vcb

KxK matrix, the covariance matrix of estimated model coefficients.

amo.mse

Scalar, mean sum of squares for errors.

amo.sse

Scalar, the sum of squares for errors.

amo.ssy

Scalar, the sum of squares for Y data.

amo.rstl

an instance of the kalmanResult structure.

Example

new;
cls;
library tsmt;

//Load data
fname = getGAUSSHome() $+ "pkgs/tsmt/examples/wpi1.dat"
data = loadd(fname);

y = data[.,1];
p=1;
d=1;
q=1;
trend=0;
const=1;

struct varmamtOut vOut;
vOut = arimaSS(y, p, d, q, trend, const);

Library

tsmt

Source

sarima_ss.src