The performance of the Akaike information criterion in threshold modelling is studied using simulation. Particular attention is paid to the effects of autoregressive parameters, the maximum order entertained, and the choice of possible candidates for the delay and threshold parameters in the procedure. © 1988 Springer-Verlag.link_to_subscribed_fulltex
Since the 1990s, the Akaike Information Criterion (AIC) and its various modifications/extensions, in...
Abstract: In this paper we propose a new class of nonlinear time series models, the threshold variab...
A b s t r a c t. The focus in the paper is on the information criteria approach and especially the A...
A bias-corrected Akaike information criterion AICC is derived for self-exciting threshold autoregres...
This paper considers information criteria as model evaluation tools for nonlinear threshold models. ...
Threshold autoregressive models in which the process is piecewise linear in the threshold space have...
Akaike Information Criterion (AIC) has been used widely as a statistical criterion to compare the ap...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
The various debates around model selection paradigms are important, but in lieu of a consensus, ther...
Threshold Autoregression is a powerful statistical tool for modeling structural nonlinear relationsh...
Wavelets are applied to identify the time delay and thresholds in open-loop threshold autoregressive...
The Modified Information Criterion (MIC) is an Akaike-like criterion which allows performance contro...
This article presents a new Bayesian modeling and information-theoretic model selection criteria for...
Stock & Watson (1999) consider the relative quality of different univariate forecasting techniques. ...
In this paper we propose a new class of nonlinear time series models, the threshold variable driven ...
Since the 1990s, the Akaike Information Criterion (AIC) and its various modifications/extensions, in...
Abstract: In this paper we propose a new class of nonlinear time series models, the threshold variab...
A b s t r a c t. The focus in the paper is on the information criteria approach and especially the A...
A bias-corrected Akaike information criterion AICC is derived for self-exciting threshold autoregres...
This paper considers information criteria as model evaluation tools for nonlinear threshold models. ...
Threshold autoregressive models in which the process is piecewise linear in the threshold space have...
Akaike Information Criterion (AIC) has been used widely as a statistical criterion to compare the ap...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
The various debates around model selection paradigms are important, but in lieu of a consensus, ther...
Threshold Autoregression is a powerful statistical tool for modeling structural nonlinear relationsh...
Wavelets are applied to identify the time delay and thresholds in open-loop threshold autoregressive...
The Modified Information Criterion (MIC) is an Akaike-like criterion which allows performance contro...
This article presents a new Bayesian modeling and information-theoretic model selection criteria for...
Stock & Watson (1999) consider the relative quality of different univariate forecasting techniques. ...
In this paper we propose a new class of nonlinear time series models, the threshold variable driven ...
Since the 1990s, the Akaike Information Criterion (AIC) and its various modifications/extensions, in...
Abstract: In this paper we propose a new class of nonlinear time series models, the threshold variab...
A b s t r a c t. The focus in the paper is on the information criteria approach and especially the A...
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