aic: The Akaike information criterion (AIC) function

Description Usage Arguments Value Author(s) Examples

Description

This function allows you to calculate the Akaike information criteria (AIC) for ARX models.

Usage

1
aic(y, x, p_max)

Arguments

y

Data vector of time series observations.

x

Matrix of data (every column represents one time series). Specify NULL or "not" if not wanted.

p_max

Maximum number of autoregressive terms to be included.

Value

p

Lag order chosen by AIC.

values

Vector containing values AIC for p = 0 up to p_max.

Author(s)

Sean Telg

Examples

1
2
data <- sim.marx(c('t',1,1), c('t',1,1),100,0.5,0.4,0.3)
aic(data$y, data$x,8)

Example output

$p
[1] 2

$values
        p = 0    p = 1    p = 2    p = 3    p = 4    p = 5    p = 6   p = 7
[1,] 9.271158 8.469428 8.466452 8.496194 8.527027 8.559339 8.590026 8.62328
        p = 8
[1,] 8.657415

MARX documentation built on May 2, 2019, 3:42 a.m.

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