Predictor distribution and forecast accuracy of threshold models

Abstract

In the present paper the predictor distribution of a SETAR ( Self Exciting Threshold Autoregressive) model (Tong and Lim, 1980) has been investigated when the lead time is greater than the threshold delay.After a brief presentation of the model under study, some relevant aspects of the density forecasts are shown highlighting how they can be used to generate more accurate predictions and to estimate an approximation of the probability density function of the SETAR predictors. The performances of competing predictors have been evaluated through a simulation study and an application to financial market data of the daily Nikkey 300 stock market returns.

Concepts of and tools for Nonlinear Time‐Series Modelling

“…This issue was investigated in Amendola and Niglio [3] who derive the conditional p.d.f. of Y t+h and the exact form of the forecastŶ c…”

“…> logpost<-function(theta, y){ > n<-length(y);a0<-theta [1];a1<-theta [2];b0<-theta [3];b1<-theta [4] > gamma<-theta [5]; c<-0; sig2<-theta…”