Journal article

A threshold mixed count time series model: Estimation and application

M Dungey, VL Martin, C Tang, A Tremayne

Studies in Nonlinear Dynamics & Econometrics | De Gruyter | Published : 2020

Abstract

A new class of integer time series models is proposed to capture the dynamic transmission of count processes over time. The approach extends existing integer mixed autoregressive-moving average models (INARMA) by allowing for shifts in the dynamics of the count process through regime changes, referred to as a threshold integer autoregressive-moving average model (TINARMA). An efficient method of moments estimator is proposed, with standard errors based on subsampling, as maximum likelihood methods are infeasible for TINARMA processes. Applying the framework to global banking crises over 200 years of data, the empirical results show strong evidence of autoregressive and moving average dynamic..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council


Funding Acknowledgements

Funder Name: Australian Research Council, Funder Id: 10.13039/501100000923, Grant Number: DP14012137.