First-order integer-valued autoregressive processes with Generalized Katz innovations

Fiche du document

Date

4 février 2022

Type de document
Périmètre
Identifiant
  • 2202.02029
Collection

arXiv

Organisation

Cornell University




Citer ce document

Federico Bassetti et al., « First-order integer-valued autoregressive processes with Generalized Katz innovations », arXiv - économie


Partage / Export

Résumé 0

A new integer-valued autoregressive process (INAR) with Generalised Lagrangian Katz (GLK) innovations is defined. We show that our GLK-INAR process is stationary, discrete semi-self-decomposable, infinite divisible, and provides a flexible modelling framework for count data allowing for under- and over-dispersion, asymmetry, and excess of kurtosis. A Bayesian inference framework and an efficient posterior approximation procedure based on Markov Chain Monte Carlo are provided. The proposed model family is applied to a Google Trend dataset which proxies the public concern about climate change around the world. The empirical results provide new evidence of heterogeneity across countries and keywords in the persistence, uncertainty, and long-run public awareness level.

document thumbnail

Par les mêmes auteurs

Sur les mêmes sujets

Sur les mêmes disciplines

Exporter en