bic: The Bayesian/Schwarz information criterion (BIC) function

In MARX: Simulation, Estimation, Model Selection and Forecasting for MARX Models

Description

This function allows you to calculate the Bayesian/Schwarz information criteria (BIC) for ARX models.

Usage

bic(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 BIC.
values	Vector containing values BIc for p = 0 up to p_max.

Author(s)

Sean Telg

Examples

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

Example output

$p
[1] 1

$values
        p = 0    p = 1    p = 2    p = 3    p = 4    p = 5    p = 6    p = 7
[1,] 9.799363 8.956975 9.010581 9.025541 9.066741 9.090684 9.089938 9.146876
        p = 8
[1,] 9.187452

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