spinar_penal: Penalized semiparametric estimation of INAR models

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

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)

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)

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.

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)

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