A MONTE CARLO STUDY ON THE PERFORMANCE OF EMPIRICAL THRESHOLD AUTOREGRESSIVE MODELS UNDER VIOLATION OF STATIONARITY ASSUMPTIONS

Abstract

One of the major importance of modeling in time series is to forecast the future values of that series. And this requires the use of appropriate method to fit the time series data which are dependent on the nature of the data. We are aware that most financial and economic data are mostly non-stationary. . The study is an extension of the work of Romsen et al (2020) which dealt with forecasting of nonlinear data that are stationary with only two threshold regimes. The study recommendations that In further research, the above models can be extended to other regimes (such as the 3 – regimes Threshold models) as well as comparing them with other regimes to understand the behaviors of the other regimes in selecting a suitable model for a data. STAR (2,1) and SETAR (2,2) are recommended to fit and forecast nonlinear data of trigonometric, exponential and polynomial forms respectively that are non-stationary.