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
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..View full abstract
Awarded by Australian Research Council
Funder Name: Australian Research Council, Funder Id: 10.13039/501100000923, Grant Number: DP14012137.