spinar_sim: Simulation of (semi)parametric integer autoregressive (INAR)...
In spINAR: (Semi)Parametric Estimation and Bootstrapping of INAR Models

spinar_sim

R Documentation

Simulation of (semi)parametric integer autoregressive (INAR) models

Description

Generating INAR(p) observations, where p \in \{1,2\}. It allows for general pmfs which can be generated parametrically or "manually" (semiparametrically).

Usage

spinar_sim(n, p, alpha, pmf, prerun = 500)

Arguments

`n`	[`integer(1)`] number of observations.
`p`	[`integer(1)`] lag of the INAR(`p`) model, where `p` `\in \{1,2\}`.
`alpha`	[`integer(p)`] vector of INAR coefficients `\code{alpha}_1,...,\code{alpha}_p`.
`pmf`	[`numeric`] vector of probability mass function `\code{pmf}_0,..., \code{pmf}_k` where `\code{pmf}_i` represents the probability of an innovation being equal to `i`.
`prerun`	[`integer(1)`] number of observations which are generated additionally and then omitted (to ensure stationarity).

Value

Vector with n INAR(p) observations.

Examples

# generate (semiparametrically) 100 INAR(1) observations with
# alpha_1 = 0.5 and a manually set pmf
spinar_sim(n = 100, p = 1, alpha = 0.5, pmf = c(0.3, 0.3, 0.2, 0.1, 0.1))

# generate 100 obervations of an INAR(2) model with
# alpha_1 = 0.2, alpha_2 = 0.3 and Poi(1)-innovations
spinar_sim(n = 100, p = 2, alpha = c(0.2, 0.3), pmf = dpois(0:20,1))

spINAR documentation built on May 29, 2024, 5:55 a.m.