Weak identification in the ESTAR model and a new model. (8th November 2012)

• Record Type: Journal Article
• Authors: Heinen, Florian; Michael, Stefanie; Sibbertsen, Philipp
• Is Part Of: Journal of time series analysis. Volume 34:Number 2(2013:Mar.)
• Page Start: 238
• Page End: 261
• Publication Date: 2012-11-08
• Subjects: Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12008
• Languages: English
• ISSNs: 0143-9782

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Determining good parameter estimates in (exponential smooth transition autoregressive) models is known to be difficult. 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 function in relation to extreme parameter combinations. This happens in particular for either very small or very large values of the error term variance. Furthermore, we introduce a new alternative model – the TSTAR model – which has similar properties as the ESTAR model but reduces the effects of the identification problem. We also derive a linearity and a unit root test for this model.</p> </abstract>