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23.07.2022 | Original Paper

A new nonlinear causality test based on single multiplicative neuron model artificial neural network: a case study for Turkey’s macroeconomic indicators

verfasst von: Erol Egrioglu, Eren Bas, Turan Cansu, M. Akif Kara

Erschienen in: Granular Computing | Ausgabe 2/2023

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Abstract

Determining causal relationships is an important task in some scientific disciplines. The linear Granger causality tests have been commonly used in the literature. Although nonlinear causality tests have been proposed in the literature, there is no causality test based on a single multiplicative neuron model artificial neural networks. The contribution of this paper is proposing a new nonlinear causality test. In this study, a nonlinear causality test is proposed based on a single multiplicative neuron model artificial neural network which is trained by particle swarm optimization. The illustrations of the proposed tests are given using some of Turkey’s macroeconomic indicator time series. The test results show similar findings to the nonlinear causality test based on a multilayer perceptron but the proposed method produces smaller p values than the multilayer perceptron-based method.

Vorheriger Artikel Adaptive hybrid fuzzy time series forecasting technique based on particle swarm optimization

Nächster Artikel Picture fuzzy soft -algebra and picture fuzzy soft measure and their applications to multi-criteria decision-making

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Titel: A new nonlinear causality test based on single multiplicative neuron model artificial neural network: a case study for Turkey’s macroeconomic indicators
verfasst von: Erol Egrioglu
Eren Bas
Turan Cansu
M. Akif Kara
Publikationsdatum: 23.07.2022
Verlag: Springer International Publishing
Erschienen in: Granular Computing / Ausgabe 2/2023
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-022-00336-z

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