ACMx: Estimation of Autoregressive Conditional Mean Models

In NTS: Nonlinear Time Series Analysis

Estimation of Autoregressive Conditional Mean Models

Description

Estimation of autoregressive conditional mean models with exogenous variables.

Usage

ACMx(y, order = c(1, 1), X = NULL, cond.dist = "po", ini = NULL)

Arguments

y	time series of counts.
order	the order of ACM model.
X	matrix of exogenous variables.
cond.dist	conditional distributions. "po" for Poisson, "nb" for negative binomial, "dp" for double Poisson.
ini	initial parameter estimates designed for use in "nb" and "dp".

Value

ACMx returns a list with components:

data	time series.
X	matrix of exogenous variables.
estimates	estimated values.
residuals	residuals.
sresi	standardized residuals.

Examples

x=rnorm(1000)*0.1
y=matrix(0,1000,1)
y[1]=2
lambda=matrix(0,1000,1)
for (i in 2:1000){
	lambda[i]=2+0.2*y[i-1]/exp(x[i-1])+0.5*lambda[i-1]
	y[i]=rpois(1,exp(x[i])*lambda[i])
}
ACMx(y,order=c(1,1),x,"po")

NTS documentation built on Sept. 25, 2023, 1:08 a.m.