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Strategies of decision-making in financial markets
Published in Ford Lumban Gaol, Natalia Filimonova, Irina Frolova, Ignatova Tatiana Vladimirovna, Inclusive Development of Society, 2020
The idea of Granger causality is that a variable X Granger-causes variable Y if variable Y can be better predicted using the historical data of both X and Y than it can be predicted using the historical data of Y alone. This is shown if the expectation of Y given the history of X is different from the unconditional expectation of Y. E(Y|Yt−k,Xt−k)≠E(Y|Yt−k),
Data Science
Published in Andrew Cook, Damián Rivas, Complexity Science in Air Traffic Management, 2016
Massimiliano Zanin, Andrew Cook, Seddik Belkoura
In recent decades, numerous metrics have been proposed to detect the presence of such relationships between sets of data representing the evolution of (real) systems. In Table 7.2, the most important are shown, grouped according to the type of relationship they are able to detect: i.e., linear and non-linear. Due to its simplicity and wide range of application, we present a short description of Granger causality. Beyond describing its characteristics, our aim is to introduce the reader to the problem of detecting causality, and thus of better defining what causality is. Granger causality is held to be one of only a few tests capable of detecting the presence of causal relationships between time series. The test is an extremely powerful tool for assessing information exchange between different elements of a system, and understanding whether the dynamics of one of them is led by the other(s). It was originally developed by Nobel Prize winner Clive Granger (Granger, 1969) and although it was applied largely in the field of economics (Hoover, 2001) it has received a lot of attention in the analysis of biomedical data (Brovelli et al., 2004; Kaminski et al., 2000; Roebroeck et al., 2005). The two axioms, on which this test is based, are stated in Table 7.3.
Air transport and economic growth: a review of the impact mechanism and causal relationships
Published in Transport Reviews, 2020
Fangni Zhang, Daniel J. Graham
Section 3 shows that the existence of a bi-directional causal relationship between air transport and economic performance largely depends on the development level of economy and aviation market. Most studies establish results based on the Granger causality analysis. However, there has been no rigorous answer to the condition under which each direction of the casual relationship holds. The heterogeneity among regions or country-pairs is investigated by, for example, Mukkala and Tervo (2013), Van De Vijver et al. (2014), and Hakim and Merkert (2016) for Europe, Asia-Pacific, and South Asia, respectively. Even though we can qualitatively infer from comparing available empirical results, the systematic and quantitative analysis is needed to reveal the implicit cause of heterogeneity. Future studies should seek to identify the underlying factor that drives the potentially inter-causal relationship. The conditional causality can be investigated using Geweke (1984)'s approach on relevant time varying covariates, such as foreign direct investment, trade, competitiveness of substitutes, and government subsidy.
Oil consumption and economic growth: Evidence from Pakistan
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2018
Ahmad Waleed, Adeel Akhtar, Ahmad Tisman Pasha
The first effort at testing for the direction of causality was proposed by Granger (1969). The Granger-causality test is an appropriate and very general technique for recognizing any existence of a causal connection between two variables. This statistical test is satisfactory and straightforward for small samples Geweke et al. (1983). According to this test, a time series (X) is said to Granger-cause another time series (Y) if the prediction error of the present series Y declines through the use of past values of X in addition to prior values of Y. For the purpose of conducting the Granger-causality test, it is important for a series of variables to be stationary.
Correlation and interaction between temperature and freeze-thaw in representative regions of Antarctica
Published in International Journal of Digital Earth, 2022
Dong Liang, Huadong Guo, Qing Cheng, Lu Zhang, Lingyi Kong
Granger causality is a statistical method for hypothesis testing that can be used to measure the mutual influence between time series by testing whether one set of time series x is the cause of changes of y in another set of time series. It has been widely adopted in fields such as economics, meteorological sciences, and neuroscience.