Details
| Original language | English |
|---|---|
| Pages (from-to) | 325-341 |
| Number of pages | 17 |
| Journal | Studies in Nonlinear Dynamics and Econometrics |
| Volume | 20 |
| Issue number | 3 |
| Early online date | 9 Dec 2015 |
| Publication status | Published - 1 Jun 2016 |
Abstract
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.
Keywords
- information criteria, Monte Carlo, nonlinear time series, threshold models
ASJC Scopus subject areas
- Mathematics(all)
- Analysis
- Social Sciences(all)
- Social Sciences (miscellaneous)
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
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In: Studies in Nonlinear Dynamics and Econometrics, Vol. 20, No. 3, 01.06.2016, p. 325-341.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Information criteria for nonlinear time series models
AU - Rinke, Saskia
AU - Sibbertsen, Philipp
PY - 2016/6/1
Y1 - 2016/6/1
N2 - 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.
AB - 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.
KW - information criteria
KW - Monte Carlo
KW - nonlinear time series
KW - threshold models
UR - http://www.scopus.com/inward/record.url?scp=84973878588&partnerID=8YFLogxK
U2 - 10.1515/snde-2015-0026
DO - 10.1515/snde-2015-0026
M3 - Article
AN - SCOPUS:84973878588
VL - 20
SP - 325
EP - 341
JO - Studies in Nonlinear Dynamics and Econometrics
JF - Studies in Nonlinear Dynamics and Econometrics
SN - 1081-1826
IS - 3
ER -