spinar_est: Semiparametric estimation of INAR models

In spINAR: (Semi)Parametric Estimation and Bootstrapping of INAR Models

Semiparametric estimation of INAR models

Description

Semiparametric estimation of the autoregressive parameters and the innovation distribution of INAR(p) models, \code{p} \in \{1,2\}. The estimation is conducted by maximizing the conditional likelihood of the model.

Usage

spinar_est(x, p)

Arguments

x	[integer] vector with integer observations.
p	[integer(1)] order of the INAR model, where \code{p} \in \{1,2\}.

Value

Vector containing the estimated coefficients \code{alpha}_1,...,\code{alpha}_p and the estimated entries of the pmf \code{pmf}_0, \code{pmf}_1,... where \code{pmf}_i represents the probability of an innovation being equal to i.

Examples

# generate data
dat1 <- spinar_sim(n = 200, p = 1, alpha = 0.5,
                   pmf = c(0.3, 0.3, 0.2, 0.1, 0.1))
dat2 <- spinar_sim(n = 200, p = 2, alpha = c(0.2, 0.3),
                   pmf = c(0.25, 0.2, 0.15, 0.1, 0.1, 0.1, 0.1))


# semiparametric estimation of INAR(1) model
spinar_est(x = dat1, p = 1)
# semiparametric estimation of INAR(2) model
spinar_est(x = dat2, p = 2)

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