spINAR (version 0.2.0)

spinar_penal: Penalized semiparametric estimation of INAR models

Description

Semiparametric penalized 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 penalized conditional likelihood of the model. If both penalization parameters are set to zero, the function coincides to the spinar_est function of this package.

Usage

spinar_penal(x, p, penal1 = 0, penal2 = 0)

Value

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

Arguments

x

[integer]
vector with integer observations.

p

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

penal1

\(L_1\) penalization parameter (default value zero results in no \(L_1\) penalization)

penal2

\(L_2\) penalization parameter (default value zero results in no \(L_2\) penalization)

Examples

# generate data
dat1 <- spinar_sim(n = 50, p = 1, alpha = 0.5,
                   pmf = c(0.3, 0.25, 0.2, 0.15, 0.1))

# penalized semiparametric estimation
spinar_penal(x = dat1, p = 1, penal1 = 0, penal2 = 0.1)