Results 11 to 20 of about 35,628 (301)
Neural Granger Causality. [PDF]
IEEE Trans Pattern Anal Mach Intell, 2022 While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions.
Tank A +4 more
europepmc +5 more sources
Bibliometric Analysis of Granger Causality Studies [PDF]
Entropy, 2023 Granger causality provides a framework that uses predictability to identify causation between time series variables. This is important to policymakers for effective policy management and recommendations.
Weng Siew Lam +3 more
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The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference [PDF]
Journal of Neuroscience Methods, 2014 Wiener-Granger causality ("G-causality") is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences.
Lionel Barnett, Anil K Seth
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NeuroImage, 2014 This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality.
Karl J Friston +2 more
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Granger-Causality in Markov Switching Models [PDF]
SSRN Electronic Journal, 2006 In this paper we propose a new parametrisation of transition probabilities that allows us to characterize and test Granger-causality in Markov switching models by means of an appropriate specification of the transition matrix. Test for independence are also provided. We illustrate our methodology with an empirical application.
BILLIO, Monica, DI SANZO S.
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Physical Review E, 2021 Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes.
Sebastiano Stramaglia +3 more
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Frontiers in Computational Neuroscience, 2021 In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct ...
Dongwei Chen +5 more
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Hubungan Antara Perkembangan Sektor Keuangan dengan Volatilitas Ekonomi di Indonesia
Economic Journal of Emerging Markets, 2009 The study is conducted to analyze the causal relationship between financial sector development and economic volatility in Indonesia during the period of 1983.2-2000.4. The study uses three kinds of variables as proxies to the financial sector development.
Romi Mulyadi H.
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Measuring Granger Causality in Quantiles [PDF]
Journal of Business & Economic Statistics, 2020 We consider measures of Granger causality in quantiles, which detect and quantify both linear and nonlinear causal effects between random variables. The measures are based on nonparametric quantile regressions and defined as logarithmic functions of restricted and unrestricted expectations of quantile check loss functions.
Song, X., Taamouti, A.
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PLoS ONE, 2023 The elderly population and economic growth have been a contentious topic among researchers. Regardless of the economic growth rate, the population and its growth have a stimulating influence on economic development.
Thaveesha Jayawardhana +5 more
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