Arbeitspapier
Information criteria for nonlinear time series models
In this paper the performance of different information criteria for simultaneous model class and lag order selection is evaluated using simulation studies. We focus on the ability of the criteria to distinguish linear and nonlinear models. In the simulation studies, we consider three different versions of the commonly known criteria AIC, SIC and AICc. In addition, we also assess the performance of WIC and evaluate the impact of the error term variance estimator. Our results confirm the findings of different authors that AIC and AICc favor nonlinear over linear models, whereas weighted versions of WIC and all versions of SIC are able to successfully distinguish linear and nonlinear models. However, the discrimination between different nonlinear model classes is more difficult. Nevertheless, the lag order selection is reliable. In general, information criteria involving the unbiased error term variance estimator overfit less and should be preferred to using the usual ML estimator of the error term variance.
- Sprache
-
Englisch
- Erschienen in
-
Series: Hannover Economic Papers (HEP) ; No. 548
- Klassifikation
-
Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Information Criteria
Nonlinear Time Series
Threshold Models
Monte Carlo
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Rinke, Saskia
Sibbertsen, Philipp
- Ereignis
-
Veröffentlichung
- (wer)
-
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
- (wo)
-
Hannover
- (wann)
-
2015
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Rinke, Saskia
- Sibbertsen, Philipp
- Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
Entstanden
- 2015
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