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Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models

Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models Abstract This article considers the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) autoregressive models. This includes the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models are discussed. Estimation by nonlinear least squares is considered as well as evaluating the properties of the estimated model. The proposed techniques are illustrated by examples using both simulated and real time series. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Statistical Association Taylor & Francis

Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models

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Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models

Teräsvirta, Timo
Journal of the American Statistical Association , Volume 89 (425): 11 – Mar 1, 1994

Abstract

Abstract This article considers the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) autoregressive models. This includes the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models are discussed. Estimation by nonlinear least squares is considered as well as evaluating the properties of the estimated model. The proposed techniques are illustrated by examples using both simulated and real time series.

 
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References (38)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1537-274X
eISSN
0162-1459
DOI
10.1080/01621459.1994.10476462
Publisher site
See Article on Publisher Site

Abstract

Abstract This article considers the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) autoregressive models. This includes the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models are discussed. Estimation by nonlinear least squares is considered as well as evaluating the properties of the estimated model. The proposed techniques are illustrated by examples using both simulated and real time series.

Journal

Journal of the American Statistical Association – Taylor & Francis

Published: Mar 1, 1994

Keywords: Canadian lynx; Linearity testing; Nonlinear autoregression; Nonlinear time series; Univariate time series modeling

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