The moments of SETARMA models

Amendola, Alessandra; Niglio, Marcella; Vitale, Cosimo Damiano

doi:10.1016/j.spl.2005.09.016

Cited by 14 publications

(5 citation statements)

References 7 publications

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“…The TAR model has also been extended to account for different specifications of the conditional mean function leading to the development of the threshold moving average (TMA: see, for example, Tong, 1978;De Gooijer, 1998;Ling et al, 2007) and the threshold autoregressive moving average (TARMA: see, for example, Ling, 1999;Amendola et al, 2006)…”

Section: Threshold Models 15mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

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Section: Threshold Models 15mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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show abstract

“…A range of variants on the threshold nonlinear model structure have also been proposed, including the threshold moving-average (TMA) (Tong 1990, De Gooijer 1998, Ling, Tong and Li 2007) and threshold autoregressive movingaverage models (TARMA) (Tong 1990, Ling 1999, Amendola, Niglio and Vitale 2006. These extensions have led to applied and theoretical works in this model class.…”

Section: Nonlinearity In the Meanmentioning

confidence: 99%

A review of threshold time series models in finance

Chen

Liu

2011

Statistics and Its Interface

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Since the pioneering work by Tong (1978Tong ( , 1983, threshold time series modelling and its applications have become increasingly important for research in economics and finance. A number of books and a vast number of research papers published in this area have been motivated by Tong's threshold models. The goal of this paper is to give a through review on the development of the family of threshold time series model in finance and to provide a streamlined approach to financial time series analysis. It covers threshold modeling, nonlinearity tests, statistical inference, diagnostic checking, and model selection, as well as applications of the threshold autoregressive model and its generalizations in finance.

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“…The local autoregressive structure makes the Gaussian hypothesis reasonable in the SETAR context. The generation of forecasts can however be affected by several other aspects, mainly related to the threshold variable, and the threshold delay, which have important implications for the form of the predictor and for its distribution [4]. …”

Section: Forecast Generationmentioning

confidence: 99%

“…> logpost<-function(theta, y){ > n<-length(y);a0<-theta [1];a1<-theta [2];b0<-theta [3];b1<-theta [4] > gamma<-theta [5]; c<-0; sig2<-theta…”

Section: Example 16mentioning

confidence: 99%

Concepts of and tools for Nonlinear Time‐Series Modelling

Amendola

Francq

2009

Handbook of Computational Econometrics

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Tools and approaches are provided for nonlinear time series modelling in econometrics. A wide range of topics is covered, including probabilistic properties, statistical inference and computational methods. The focus is on the applications but the ideas of the mathematical arguments are also provided. Techniques and concepts are illustrated by various examples, Monte Carlo experiments and a real application. 1

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The moments of SETARMA models

Cited by 14 publications

References 7 publications

Forecasting: theory and practice

Forecasting: theory and practice

A review of threshold time series models in finance

Concepts of and tools for Nonlinear Time‐Series Modelling

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