We consider a recently proposed class of nonlinear time series models and focus mainly onmisspecification testing for models of such type. Following the modeling cycle for nonlineartime series models of specification, estimation and evaluation we first treat how to choosean adequate transition...

In ESTAR models it is usually dfficult to determine parameter estimates, as it can be observedin the literature. We show that the phenomena of getting strongly biased estimators is aconsequence of the so-called identication problem, the problem of properly distinguishing thetransition function...

Determining good parameter estimates in ESTAR models is known to be difficult. We showthat the phenomena of getting strongly biased estimators is a consequence of the so-calledidentication problem, the problem of properly distinguishing the transition function in relationto extreme parameter...

While it is widely agreed that Purchasing Power Parity (PPP) holds as a long-runconcept the specific dynamic driving the process is largely build upon a priori economicbelief rather than a thorough statistical modeling procedure. The two prevailing timeseries models, i.e. the exponential smooth...

We consider a recently proposed class of nonlinear time series models and focus mainly on misspecification testing for models of such type. Following the modeling cycle for nonlinear time series models of specification, estimation and evaluation we first treat how to choose an adequate...

We examine the asymptotic behavior of unit root tests against nonlinear alternatives of the exponential smooth transition type if the data is erroneously nonlinearly transformed. We show analytically and by a Monte Carlo study that the probability of rejecting the correct null of a random walk...

We consider the detection of a change in persistence of a long range dependent time series. The usual approach is to use one-shot tests to detect a change in persistence a posteriori in a historical data set. However, as breaks can occur at any given time and data arrives steadily it is...

We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of...

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 consider a recently proposed class of nonlinear time series models and focus mainly on misspecification testing for models of such type. Following the modeling cycle for nonlinear time series models of specification, estimation and evaluation we first treat how to choose an adequate...