On the Identifiability Conditions in Some Nonlinear Time Series Models

TL;DR

This paper investigates the conditions under which certain nonlinear time series models, including smooth transition GARCH and nonlinear Poisson autoregressive models, can be uniquely identified from data.

Contribution

It provides new sufficient conditions that ensure the identifiability of several complex nonlinear time series models.

Findings

Derived sufficient conditions for model identifiability

Focused on smooth transition GARCH and nonlinear Poisson models

Enhanced understanding of model uniqueness in nonlinear time series

Abstract

In this study, we consider the identifiability problem for nonlinear time series models. Special attention is paid to smooth transition GARCH, nonlinear Poisson autoregressive, and multiple regime smooth transition autoregressive models. Some sufficient conditions are obtained to establish the identifiability of these models.