Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 238-261 |
| Seitenumfang | 24 |
| Fachzeitschrift | Journal of time series analysis |
| Jahrgang | 34 |
| Ausgabenummer | 2 |
| Frühes Online-Datum | 8 Nov. 2012 |
| Publikationsstatus | Veröffentlicht - März 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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Entscheidungswissenschaften (insg.)
- Statistik, Wahrscheinlichkeit und Ungewissheit
- Mathematik (insg.)
- Angewandte Mathematik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Journal of time series analysis, Jahrgang 34, Nr. 2, 03.2013, S. 238-261.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Weak identification in the ESTAR model and a new model
AU - Heinen, Florian
AU - Michael, Stefanie
AU - Sibbertsen, Philipp
PY - 2013/3
Y1 - 2013/3
N2 - 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.
AB - 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.
KW - Linearity testing
KW - Nonlinearities
KW - Real exchange rates
KW - Smooth transition
KW - Unit root testing
UR - http://www.scopus.com/inward/record.url?scp=84874193970&partnerID=8YFLogxK
U2 - 10.1111/jtsa.12008
DO - 10.1111/jtsa.12008
M3 - Article
AN - SCOPUS:84874193970
VL - 34
SP - 238
EP - 261
JO - Journal of time series analysis
JF - Journal of time series analysis
SN - 0143-9782
IS - 2
ER -