sarimaSS

Purpose

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

Format

vOut = sarimaSS(y, p, d, q, P_s, D_s, Q_s, s, trend, const)

Parameters:

• y (Nx1 vector) – data.

• p (Scalar) – the autoregressive order.

• d (Scalar) – the order of differencing.

• q (Scalar) – the moving average order.

• P_s (Scalar) – the seasonal autoregressive order.

• D_S (Scalar) – the seasonal order of differencing.

• Q_s (Scalar) – the seasonal moving average order.

• s (Scalar) – the seasonal frequency term.

• 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:

amo (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;

airline = loadd( getGAUSSHome() $+ "pkgs/tsmt/examples/airline.dat");

// Transform data
y = ln(airline);

p = 0;
d = 1;
q = 1;

P_s = 0;
D_s = 1;
Q_s = 1;
s=12;

trend = 0;
const = 0;

struct arimamtOut amo;
amo = sarimaSS( y, p, d, q, P_s, D_s, Q_s, s, trend, const );

Library

tsmt

Source

sarima_ss.src