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A threshold mixed count time series model: Estimation and application

Citation

Dungey, M and Martin, VL and Tang, C and Tremayne, A, A threshold mixed count time series model: Estimation and application, Studies in Nonlinear Dynamics and Econometrics pp. 1-18. ISSN 1081-1826 (2019) [Refereed Article]

Copyright Statement

Copyright 2019 The Academy of Spinal Cord Injury Professionals, Inc.

DOI: doi:10.1515/snde-2018-0029

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 dynamics which vary across systemic and nonsystemic regimes over time. Coherent forecast distributions are also produced with special attention given to the Great Depression and the more recent Global Financial Crisis.

Item Details

Item Type:Refereed Article
Keywords:banking crises, binomial thinning, count time series, efficient method of moments, threshold
Research Division:Economics
Research Group:Econometrics
Research Field:Economic Models and Forecasting
Objective Division:Economic Framework
Objective Group:Microeconomics
Objective Field:Microeconomics not elsewhere classified
UTAS Author:Dungey, M (Professor Mardi Dungey)
ID Code:137007
Year Published:2019
Deposited By:Economics and Finance
Deposited On:2020-01-28
Last Modified:2020-05-06
Downloads:0

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