In ESTAR models it is usually difficult to determine parameter estimates, as it can be observed in the literature. We show that the phenomena of getting strongly biased estimators is a consequence of the so-called identification problem, the problem of properly distinguishing the transition...

We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor's 500...

This paper extends the family of smooth transition autoregressive (STAR) models by proposing a speci.cation in which the autoregressive parameters follow random walks. The random walks in the parameters capture permanent structural change within a regime switching framework, but in contrast to...