Semiparametric and parametric estimation of INAR models including a finite sample refinement for the semiparametric setting, different procedures to bootstrap INAR data and flexible simulation of INAR data.
The package provides a flexible simulation of INAR data by inserting a user-defined
pmf argument in the spinar_sim function. Using spinar_est,
it allows for semiparametric estimation of the INAR model along Drost et al. (2009)
and additionally, it includes a small sample refinement spinar_penal
(Faymonville et al., 2022) together with a validation of the upcoming penalization
parameters (spinar_penal_val). Furthermore, it contains a semiparametric
INAR bootstrap procedure implemented in spinar_boot (Jentsch and Weiß, 2017).
In addition to the semiparametric model, the package also allows for parametric simulation
(spinar_sim), parametric estimation (spinar_est_param) and
parametric bootstrapping (spinar_boot) of INAR data.
Maintainer: Maxime Faymonville faymonville@statistik.tu-dortmund.de (ORCID)
Authors:
Javiera Riffo javiera.riffo@tu-dortmund.de (ORCID)
Jonas Rieger rieger@statistik.tu-dortmund.de (ORCID)
Carsten Jentsch jentsch@statistik.tu-dortmund.de (ORCID)
Other contributors:
Christian H. Weiß weissc@hsu-hh.de (ORCID) [contributor]
Faymonville, M., Jentsch, C., Weiß, C.H. and Aleksandrov, B. (2022). "Semiparametric Estimation of INAR Models using Roughness Penalization". Statistical Methods & Applications. tools:::Rd_expr_doi("10.1007/s10260-022-00655-0").
Jentsch, C. and Weiß, C. H. (2017), “Bootstrapping INAR Models”. Bernoulli 25(3), pp. 2359--2408. tools:::Rd_expr_doi("10.3150/18-BEJ1057").
Drost, F., Van den Akker, R. and Werker, B. (2009), “Efficient estimation of auto-regression parameters and innovation distributions for semiparametric integer-valued AR(p) models”. Journal of the Royal Statistical Society. Series B 71(2), pp. 467--485. tools:::Rd_expr_doi("10.1111/j.1467-9868.2008.00687.x").
Useful links: