Weak Identification in the ESTAR Model and a New Model

24 Pages Posted: 22 Feb 2013

See all articles by Florian Heinen

Florian Heinen

affiliation not provided to SSRN

Stefanie Michael

affiliation not provided to SSRN

Philipp Sibbertsen

University of Hannover

Date Written: March 2013

Abstract

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.

Keywords: Nonlinearities, smooth transition, linearity testing, unit root testing, real exchange rates

JEL Classification: C12, C22, C52

Suggested Citation

Heinen, Florian and Michael, Stefanie and Sibbertsen, Philipp, Weak Identification in the ESTAR Model and a New Model (March 2013). Journal of Time Series Analysis, Vol. 34, Issue 2, pp. 238-261, 2013. Available at SSRN: https://ssrn.com/abstract=2222473 or http://dx.doi.org/10.1111/jtsa.12008

Florian Heinen (Contact Author)

affiliation not provided to SSRN

No Address Available

Stefanie Michael

affiliation not provided to SSRN

No Address Available

Philipp Sibbertsen

University of Hannover ( email )

Welfengarten 1
D-30167 Hannover, 30167
Germany

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