The Predictive Distributions of Thinning-based Count Processes

Abstract : This paper shows that the term structure of conditional, or predictive distributions allows for closed form expression in a large family of (possibly higher-order, or infinite order) thinning-based count processes such as INAR(p), INARCH(p), NBAR(p), and INGARCH(1,1). Such predictive distributions are currently often deemed intractable by the literature and existing approximation methods are usually time consuming and induce approximation errors. In this paper, we propose a Taylor's expansion algorithm for these predictive distributions, which is both exact and fast. Through extensive simulation exercises, we demonstrate its advantages with respect to existing methods in terms of the computational gain and/or precision.
Document type :
Journal articles
Complete list of metadatas

Cited literature [47 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02419018
Contributor : Yang Lu <>
Submitted on : Thursday, December 19, 2019 - 11:40:53 AM
Last modification on : Saturday, February 15, 2020 - 1:53:05 AM

File

estimation and forecasting_bli...
Files produced by the author(s)

Identifiers

Citation

Yang Lu. The Predictive Distributions of Thinning-based Count Processes. Scandinavian Journal of Statistics, Wiley, In press, ⟨10.1111/sjos.12438⟩. ⟨hal-02419018⟩

Share

Metrics

Record views

27

Files downloads

60