Journal articles: 'Granger causality'

1

Seth, Anil. "Granger causality." Scholarpedia 2, no. 7 (2007): 1667. http://dx.doi.org/10.4249/scholarpedia.1667.

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Friston, Karl J., André M. Bastos, Ashwini Oswal, Bernadette van Wijk, Craig Richter, and Vladimir Litvak. "Granger causality revisited." NeuroImage 101 (November 2014): 796–808. http://dx.doi.org/10.1016/j.neuroimage.2014.06.062.

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Malekpour, Sheida, and William A. Sethares. "Conditional Granger causality and partitioned Granger causality: differences and similarities." Biological Cybernetics 109, no. 6 (October 16, 2015): 627–37. http://dx.doi.org/10.1007/s00422-015-0665-3.

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4

He, Zonglu, and Koichi Maekawa. "On spurious Granger causality." Economics Letters 73, no. 3 (December 2001): 307–13. http://dx.doi.org/10.1016/s0165-1765(01)00498-0.

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Al-Sadoon, Majid M. "Testing subspace Granger causality." Econometrics and Statistics 9 (January 2019): 42–61. http://dx.doi.org/10.1016/j.ecosta.2017.08.003.

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6

Chao, John, Valentina Corradi, and Norman R. Swanson. "OUT-OF-SAMPLE TESTS FOR GRANGER CAUSALITY." Macroeconomic Dynamics 5, no. 4 (September 2001): 598–620. http://dx.doi.org/10.1017/s1365100501023070.

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Clive W.J. Granger has summarized his personal viewpoint on testing for causality in numerous articles over the past 30 years and has outlined what he considers to be a useful operational version of his original definition of Granger causality, which he notes is partially alluded to in the Ph.D. dissertation of Norbert Wiener. This operational version of Granger causality is based on a comparison of the one-step-ahead predictive ability of competing models. However, Granger concludes his discussion by noting that it is common practice to test for Granger causality using in-sample F-tests. The practice of using in-sample type Granger causality tests continues to be prevalent. In this paper we develop simple (nonlinear) out-of-sample predictive ability tests of the Granger non-causality null hypothesis. In addition, Monte Carlo experiments are used to investigate the finite sample properites of the test. An empirical illustration shows that the choice of in-sample versus out-of-sample Granger causality tests can crucially affect the conclusions about the predictive content of money for output.

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Ahmadi, Salman, Girish N. Nair, and Erik Weyer. "Granger causality from quantized measurements." Automatica 142 (August 2022): 110371. http://dx.doi.org/10.1016/j.automatica.2022.110371.

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Elsner, James B. "Granger causality and Atlantic hurricanes." Tellus A: Dynamic Meteorology and Oceanography 59, no. 4 (January 1, 2007): 476–85. http://dx.doi.org/10.1111/j.1600-0870.2007.00244.x.

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9

Tastan, Hüseyin. "Testing for Spectral Granger Causality." Stata Journal: Promoting communications on statistics and Stata 15, no. 4 (December 2015): 1157–66. http://dx.doi.org/10.1177/1536867x1501500411.

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Angelini, Leonardo, Mario Pellicoro, and Sebastiano Stramaglia. "Granger causality for circular variables." Physics Letters A 373, no. 29 (June 2009): 2467–70. http://dx.doi.org/10.1016/j.physleta.2009.05.009.

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11

Granderson, Gerald, and Carl Linvill. "Regulation, efficiency, and Granger causality." International Journal of Industrial Organization 20, no. 9 (November 2002): 1225–45. http://dx.doi.org/10.1016/s0167-7187(01)00094-7.

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12

Marinazzo, Daniele, Wei Liao, Huafu Chen, and Sebastiano Stramaglia. "Nonlinear connectivity by Granger causality." NeuroImage 58, no. 2 (September 2011): 330–38. http://dx.doi.org/10.1016/j.neuroimage.2010.01.099.

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Petrović, Ljiljana, Sladana Dimitrijević, and Dragana Valjarević. "Granger causality and stopping times*." Lithuanian Mathematical Journal 56, no. 3 (July 2016): 410–16. http://dx.doi.org/10.1007/s10986-016-9325-0.

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Kim, Jong-Min, Namgil Lee, and Sun Young Hwang. "A Copula Nonlinear Granger Causality." Economic Modelling 88 (June 2020): 420–30. http://dx.doi.org/10.1016/j.econmod.2019.09.052.

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Balboa, Marina, Germán López-Espinosa, and Antonio Rubia. "Granger causality and systemic risk." Finance Research Letters 15 (November 2015): 49–58. http://dx.doi.org/10.1016/j.frl.2015.08.003.

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PAKSOY, H. Mustafa. "TÜRKİYE'DE KAMU HARCAMALARI VE GÖÇ İLİŞKİSİ: GRANGER NEDENSELLİK ANALİZİ." JOURNAL OF PURE SOCIAL SCIENCES (PURESOC) 5, no. 9 (December 24, 2024): 28–41. https://doi.org/10.5281/zenodo.14551929.

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Abstract In recent years, migration waves have become more frequent due to the impact of wars. Migration waves have the highest impact on the economy.  With globalization, its size and impact are gaining more and more meaning with each passing day. Therefore, the study area of migration has expanded and has been associated with important macroeconomic variables. In this paper, the relationship between migration and public expenditures is analyzed for Türkiye. International migration, public expenditures and economic growth variables are used in the model. Annual data for the period 1996-2022 were used to estimate the variables. In order to have information about the direction of the variables, Granger causality test is applied. However, before the Granger causality test, the stationarity of the variables should be examined. ADF test is preferred to test whether there is a unit root in the variables. According to the ADF test results, the variables are stationary in the first difference. According to the Granger causality test results, there is unidirectional causality from migration to public expenditures and from economic growth to public expenditures. According to the last finding, unilateral causality was found from economic growth to migration variable. Keywords: Mıgration, Public Expenditures, Grnager Causalıty Analysis, Türkiye JEL Classification: F1, F31

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Kumar Tiwari, Aviral, A. P. Tiwari, and Bharti Pandey. "Fiscal Deficit and Inflation: What Causes What? The Case of India." Journal of International Business and Economy 13, no. 1 (July 1, 2012): 57–81. http://dx.doi.org/10.51240/jibe.2012.1.3.

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This study has made an attempt to examine the direction of causality among the fiscal deficit, government expenditure, money supply, and inflation. In the present study we have employed Dolado and L체tkepohl (DL) (1996) and standard Granger-causality approach to examine the direction of the causality among the test variables. However, we have found conflicting results for India. Causality analysis based on DL approach suggests that both government expenditure and money supply Granger-cause fiscal deficit while standard Granger-causality test indicates that only government expenditure Granger-cause fiscal deficit. And money supply Granger-cause government expenditure and fiscal deficit Granger-cause money supply.

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FUJITA, ANDRÉ, JOÃO RICARDO SATO, KANAME KOJIMA, LUCIANA RODRIGUES GOMES, MASAO NAGASAKI, MARI CLEIDE SOGAYAR, and SATORU MIYANO. "IDENTIFICATION OF GRANGER CAUSALITY BETWEEN GENE SETS." Journal of Bioinformatics and Computational Biology 08, no. 04 (August 2010): 679–701. http://dx.doi.org/10.1142/s0219720010004860.

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Wiener and Granger have introduced an intuitive concept of causality (Granger causality) between two variables which is based on the idea that an effect never occurs before its cause. Later, Geweke generalized this concept to a multivariate Granger causality, i.e. n variables Granger-cause another variable. Although Granger causality is not "effective causality" in the Aristothelic sense, this concept is useful to infer directionality and information flow in observational data. Granger causality is usually identified by using VAR (Vector Autoregressive) models due to their simplicity. In the last few years, several VAR-based models were presented in order to model gene regulatory networks. Here, we generalize the multivariate Granger causality concept in order to identify Granger causalities between sets of gene expressions, i.e. whether a set of n genes Granger-causes another set of m genes, aiming at identifying the flow of information between gene networks (or pathways). The concept of Granger causality for sets of variables is presented. Moreover, a method for its identification with a bootstrap test is proposed. This method is applied in simulated and also in actual biological gene expression data in order to model regulatory networks. This concept may be useful for the understanding of the complete information flow from one network or pathway to the other, mainly in regulatory networks. Linking this concept to graph theory, sink and source can be generalized to node sets. Moreover, hub and centrality for sets of genes can be defined based on total information flow. Another application is in annotation, when the functionality of a set of genes is unknown, but this set is Granger-caused by another set of genes which is well studied. Therefore, this information may be useful to infer or construct some hypothesis about the unknown set of genes.

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Qian, Jing, Wenpo Yao, Dengxuan Bai, Qiong Wang, Shuwang Wang, Ang Zhou, Wei Yan, and Jun Wang. "Depressed MEG causality analysis based on polynomial kernel Granger causality." AIP Advances 13, no. 3 (March 1, 2023): 035234. http://dx.doi.org/10.1063/5.0142058.

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In this study, we employ the Granger causality of a polynomial kernel to identify the coupling causality of depressed magnetoencephalography (MEG). We collect MEG under positive, neutral, and negative emotional stimuli and focus on the β-band activities. According to test results, depressed people display stronger left–right symmetrical interconnection in their prefrontal and occipital lobes under nonpositive stimuli(namely neutral and negative stimuli), indicating that they are more sensitive to nonpositive stimuli. The intensity of the right occipital information flow is higher in depressed people. We also see the Granger causality index increased in the occipital–frontal areas of depressed patients under negative stimuli. In general, detecting the polynomial kernel Granger causality of the MEG can effectively characterize the strength of the interconnected brain regions in depressed patients, which can be used as a clinical diagnosis aid.

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20

Kacemi, Tarek, and Sallahuddin Hassan. "Causal Linkage between Inflation and Unemployment: An evidence from the Selected MENA Countries." Pakistan Journal of Humanities and Social Sciences 6, no. 1 (March 31, 2018): 121–31. http://dx.doi.org/10.52131/pjhss.2018.0601.0037.

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The current study measures the causal association between inflation and unemployment employing Phillips Curve approach from 1990 until 2016 for selected MENA countries. Granger causality and the heterogeneous causality methods for Panel are employed by this study as proposed by Dumitrescu and Hurlin. This causality test has an advantage over the panel Granger causality as it considers two dimensions of heterogeneity. The finding revealed a unidirectional causality between unemployment and inflation with Panel Dumitrescu and Hurlin Granger causality but not in the panel Granger causality test. Therefore, the governments should choose to stabilize inflation rate or reduce unemployment rate.

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21

Kwon, Tae Yeon. "Causality Change in Life Insurance Demand: Focusing on Financial Market and Insurance Provider Factors." Korean Data Analysis Society 25, no. 1 (February 28, 2023): 173–84. http://dx.doi.org/10.37727/jkdas.2022.25.1.173.

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This paper examined the Granger causality and changes in its intensity for Korea life insurance demand, focusing on the financial market and insurance provider factors. The significant causality ratio (SCR) in Kwon (2018), which can measure the intensity as well as the change in the significance of Granger causality, was applied. The intensity of the Granger causality of the three-year treasury bond rate, three-year AA corporate bond rate, KOSPI200 index, and number of planners was examined for each life insurance demand from January 1997 to May 2022. The change in the intensity of Granger causality for life insuance demand were visualized through the time plot of SCR by demand factor and life insurance type which are endowment, pure-endowment insurances and insurance against death, respectively. As a result, it was confirmed that the existence and intensity of Granger causality of each demand factor for each insurance type changed, and the pattern of change varies by factor and life insurance type. The factor with higher Granger causality than other periods in the global financial crisis is the interest rate and the Granger causality of the number of planners showed the opposite pattern. The Granger causality of the stock index for insurance against death was lower than that of other insurances.

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22

Xu, Xiaojie. "Contemporaneous and Granger causality among US corn cash and futures prices." European Review of Agricultural Economics 46, no. 4 (November 2, 2018): 663–95. http://dx.doi.org/10.1093/erae/jby036.

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AbstractThis paper examines contemporaneous and Granger causality among US corn futures and seven cash prices from major producing states for January 2006–March 2011. Causal flows from futures to cash prices are identified with contemporaneous and in-sample Granger causality tests but not with the out-of-sample Granger causality test. While no interstate in-sample or out-of-sample Granger causality is found, contemporaneous causal linkages are revealed. No causality from cash to futures prices is determined.

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23

Baum, Christopher F., Stan Hurn, and Jesús Otero. "Testing for time-varying Granger causality." Stata Journal: Promoting communications on statistics and Stata 22, no. 2 (June 2022): 355–78. http://dx.doi.org/10.1177/1536867x221106403.

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The concept of Granger causality is an important tool in applied macroeconomics. Recently, recursive econometric methods have been developed to analyze the temporal stability of Granger-causal relationships. This article offers an implementation of these recursive procedures in Stata. An empirical example illustrates their use in analyzing the temporal stability of Granger causality among key U.S. macroeconomic series.

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Hu, Sanqing, Yu Cao, Jianhai Zhang, Wanzeng Kong, Kun Yang, Yanbin Zhang, and Xun Li. "More discussions for granger causality and new causality measures." Cognitive Neurodynamics 6, no. 1 (September 27, 2011): 33–42. http://dx.doi.org/10.1007/s11571-011-9175-8.

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25

Piotr, Fiszeder, and Orzeszko Witold. "Nonlinear Granger causality between grains and livestock." Agricultural Economics (Zemědělská ekonomika) 64, No. 7 (July 17, 2018): 328–36. http://dx.doi.org/10.17221/376/2016-agricecon.

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Linear and nonlinear Granger causality between three grains: corn, soybean, wheat and two livestock commodities: live cattle and lean hogs, was verified. Weak evidence of linear causal relationships was found, supporting the results published in other studies. However, strong nonlinear causal relationships between grain and livestock returns were found, which had not yet been documented in the literature on this subject. The revealed relationships have different patterns and features, and in some cases, they arise from second moment dependencies, but nonlinearities of a different type were also found. Most of the discovered nonlinear relationships are bidirectional.

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Zhao, Ling Di, and Qing Hao. "The Relationship between Oil Consumption and Employment – Based on Data from Twenty Provinces." Advanced Materials Research 709 (June 2013): 764–69. http://dx.doi.org/10.4028/www.scientific.net/amr.709.764.

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This paper discusses the relationship between oil consumption and employment growth in 20 provinces of China. In the study, we mainly use the methods of co-integration and Granger causality. The result indicateds that in different kinds of provinces, there are different kinds of Granger causality between oil consumption and employment. In some provinces, such as Shandong province, Liaoning province and Zhejiang province, there exists one-way Granger causality between the two variables; in Jiangxi province, there exists two-way Granger Causality; while in other provinces, there is no Granger Causality among them. The result is highly correlated with the industrial structure, the structure of economic development and the preferences of energy resources in certain cities.

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Peng, Wei. "DLI: A Deep Learning-Based Granger Causality Inference." Complexity 2020 (June 9, 2020): 1–6. http://dx.doi.org/10.1155/2020/5960171.

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Integrating autoencoder (AE), long short-term memory (LSTM), and convolutional neural network (CNN), we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference (DLI). Two contributions of the proposed DLI are to reveal the Granger causality between the bitcoin price and S&P index and to forecast the bitcoin price and S&P index with a higher accuracy. Experimental results demonstrate that there is a bidirectional but asymmetric Granger causality between the bitcoin price and S&P index. And the DLI performs a superior prediction accuracy by integrating variables that have causalities with the target variable into the prediction process.

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Lam, Weng Siew, Weng Hoe Lam, Saiful Hafizah Jaaman, and Pei Fun Lee. "Bibliometric Analysis of Granger Causality Studies." Entropy 25, no. 4 (April 7, 2023): 632. http://dx.doi.org/10.3390/e25040632.

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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. Granger causality is recognized as the primary advance on the causation problem. The objective of this paper is to conduct a bibliometric analysis of Granger causality publications indexed in the Web of Science database. Harzing’s Publish or Perish and VOSviewer were used for performance analysis and science mapping. The first paper indexed was published in 1981 and there has been an upward trend in the annual publication of Granger causality studies which are shifting towards the areas of environmental science, energy, and economics. Most of the publications are articles and proceeding papers under the areas of business economics, environmental science ecology, and neurosciences/neurology. China has the highest number of publications while the United States has the highest number of citations. England has the highest citation impact. This paper also constructed country co-authorship, co-analysis of cited references, cited sources, and cited authors, keyword co-occurrence, and keyword overlay visualization maps.

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Suryadi, Lock Yue Chew, and Yew-Soon Ong. "Granger causality using Jacobian in neural networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 2 (February 2023): 023126. http://dx.doi.org/10.1063/5.0106666.

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Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here, we introduce JGC (Jacobian Granger causality), a neural network-based approach to Granger causality using the Jacobian as a measure of variable importance, and propose a variable selection procedure for inferring Granger causal variables with this measure, using criteria of significance and consistency. The resulting approach performs consistently well compared to other approaches in identifying Granger causal variables, the associated time lags, as well as interaction signs. In addition, we also discuss the need for contemporaneous variables in Granger causal modeling as well as how these neural network-based approaches reduce the impact of nonseparability in dynamical systems, a problem where predictive information on a target variable is not unique to its causes, but also contained in the history of the target variable itself.

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Barika, Barika, Armelly Armelly, and Benardin Benardin. "TINJAUAN KAUSALITAS INDIKATOR MAKROEKONOMI DI PROVINSI BENGKULU." Convergence: The Journal of Economic Development 2, no. 2 (February 5, 2021): 118–32. http://dx.doi.org/10.33369/convergence-jep.v2i2.12536.

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The aims of this research are to determine the statistical causality between poverty, education level, economic growth, investment and income inequality in Bengkulu province. To analyze how the influence of education level, economic growth, investment and income inequality on poverty in Bengkulu province. This research are use granger causality test method and Panel Multiple regression. The result shows the variables have causal relations are income inequality with economic growth, income inequality with investment. Panel data regression results show that education, economic growth, and investment variables significantly influence poverty in Bengkulu province. Keywords : Granger Causality1, Education2, Economic Growth3, Invesment 4, Poverty 5

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31

CEVHER, Erdogan. "causfinder: An R package for Systemwise Analysis of Conditional and Partial Granger Causalities." International Journal of Science and Advanced Technology 4, no. 10 (October 31, 2014): 6–15. https://doi.org/10.5281/zenodo.35599.

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Multivariate Granger causality analysis is the study where at least one of the sets of independent and dependent variables includes more than 1 variable when these variables are conditioned on third set of variables in the analyzed system. causfinder reveals – via systemwise approaching the G-causalities – all conditional and partial Granger causalities in the system in any desired pattern formed by various distributions of these variables to these three sets. It also reveals the character of variables from more independent variables to dependent ones with the degrees and directions of the G-causality relations.

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Jang, Hyuna, Jong-Min Kim, and Hohsuk Noh. "Vine copula Granger causality in mean." Economic Modelling 109 (April 2022): 105798. http://dx.doi.org/10.1016/j.econmod.2022.105798.

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Sobieraj, Marcin, and Piotr Setny. "Granger Causality Analysis of Chignolin Folding." Journal of Chemical Theory and Computation 18, no. 3 (February 15, 2022): 1936–44. http://dx.doi.org/10.1021/acs.jctc.1c00945.

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34

Ohama, Gan. "GRANGER TYPE CAUSALITY FOR NONLINEAR DATA." Bulletin of informatics and cybernetics 34, no. 2 (December 2002): 105–32. http://dx.doi.org/10.5109/13513.

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Attanasio, Alessandro, Antonello Pasini, and Umberto Triacca. "Granger Causality Analyses for Climatic Attribution." Atmospheric and Climate Sciences 03, no. 04 (2013): 515–22. http://dx.doi.org/10.4236/acs.2013.34054.

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Tissot, Gilles, Adrian Lozano-Durán, Laurent Cordier, Javier Jiménez, and Bernd R. Noack. "Granger causality in wall-bounded turbulence." Journal of Physics: Conference Series 506 (April 16, 2014): 012006. http://dx.doi.org/10.1088/1742-6596/506/1/012006.

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Song, Xiaojun, and Abderrahim Taamouti. "Measuring Nonlinear Granger Causality in Mean." Journal of Business & Economic Statistics 36, no. 2 (April 28, 2017): 321–33. http://dx.doi.org/10.1080/07350015.2016.1166118.

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Chen, Yi-Ting. "Testing for Granger Causality in Moments." Oxford Bulletin of Economics and Statistics 78, no. 2 (June 25, 2015): 265–88. http://dx.doi.org/10.1111/obes.12108.

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Zhou, Douglas, Yaoyu Zhang, Yanyang Xiao, and David Cai. "Reliability of the Granger causality inference." New Journal of Physics 16, no. 4 (April 22, 2014): 043016. http://dx.doi.org/10.1088/1367-2630/16/4/043016.

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White, H., and X. Lu. "Granger Causality and Dynamic Structural Systems." Journal of Financial Econometrics 8, no. 2 (March 19, 2010): 193–243. http://dx.doi.org/10.1093/jjfinec/nbq006.

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Dhamala, Mukesh, Hualou Liang, Steven L. Bressler, and Mingzhou Ding. "Granger-Geweke causality: Estimation and interpretation." NeuroImage 175 (July 2018): 460–63. http://dx.doi.org/10.1016/j.neuroimage.2018.04.043.

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Troster, Victor. "Testing for Granger-causality in quantiles." Econometric Reviews 37, no. 8 (June 4, 2016): 850–66. http://dx.doi.org/10.1080/07474938.2016.1172400.

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Luo, Qiang, Tian Ge, and Jianfeng Feng. "Granger causality with signal-dependent noise." NeuroImage 57, no. 4 (August 2011): 1422–29. http://dx.doi.org/10.1016/j.neuroimage.2011.05.054.

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Petrov, Y. "A cause for non-Granger causality." Journal of Vision 13, no. 9 (July 25, 2013): 1194. http://dx.doi.org/10.1167/13.9.1194.

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Webb, J. Taylor, Michael A. Ferguson, Jared A. Nielsen, and Jeffrey S. Anderson. "BOLD Granger Causality Reflects Vascular Anatomy." PLoS ONE 8, no. 12 (December 13, 2013): e84279. http://dx.doi.org/10.1371/journal.pone.0084279.

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Billio, Monica, and Silvio Di Sanzo. "Granger-causality in Markov switching models." Journal of Applied Statistics 42, no. 5 (January 22, 2015): 956–66. http://dx.doi.org/10.1080/02664763.2014.993367.

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姚, 牧芸. "Dynamic Regularized Granger Causality Learning Method." Hans Journal of Data Mining 15, no. 02 (2025): 184–200. https://doi.org/10.12677/hjdm.2025.152016.

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Rossi, Barbara, and Yiru Wang. "Vector autoregressive-based Granger causality test in the presence of instabilities." Stata Journal: Promoting communications on statistics and Stata 19, no. 4 (December 2019): 883–99. http://dx.doi.org/10.1177/1536867x19893631.

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In this article, we review Granger causality tests that are robust to the presence of instabilities in a vector autoregressive framework. We also introduce the gcrobustvar command, which illustrates the procedure in Stata. In the presence of instabilities, the Granger causality robust test is more powerful than the traditional Granger causality test.

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Lahmiri, Salim. "Causality Between Brent and West Texas Intermediate: The Effects of COVID-19 Pandemic and Russia–Ukraine War." Commodities 4, no. 1 (February 28, 2025): 2. https://doi.org/10.3390/commodities4010002.

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The article analyzes the Granger-based causal relationship between two major crude oil markets, namely Brent and West Texas Intermediate (WTI), by using the standard vector autoregression (VAR) framework. In this regard, the effects of the COVID-19 pandemic and the Russia–Ukraine war on causality between Brent and WTI are examined. The empirical results from Granger-causality tests show (a) strong causality from Brent to WTI during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (b) no causality from WTI to Brent during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (c) no causality from Brent to WTI during the COVID-19 pandemic, (d) evidence of causality from WTI to Brent during the COVID-19 pandemic, and (e) no evidence of causality from both markets during the period of Russia–Ukraine war. In addition, causality tests in quantiles support results from the linear Granger causality tests in general. However, contrary to the standard linear causality test, the quantile-in-regression causality test shows that Brent returns cause WTI returns during the pandemic period and WTI returns cause Brent returns before the pandemic. Furthermore, the results from the time-varying Granger causality tests support all conclusions from the standard linear (and static) Granger causality test, except the hypothesis that Brent causes WTI during the pandemic. Moreover, the time-varying Granger tests show evidence that causality between Brent and WTI clearly varies across the pandemic and war periods. Revealing the causalities between Brent and WTI across periods of economic and political stability, pandemic, and war would help policymakers develop appropriate energy policy and help investors determine appropriate risk management actions.

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Zhang, Qiyi, Chuanlin Zhang, and Shuangqin Cheng. "Wavelet Multiscale Granger Causality Analysis Based on State Space Models." Symmetry 15, no. 6 (June 20, 2023): 1286. http://dx.doi.org/10.3390/sym15061286.

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Abstract:

Granger causality (GC) is a popular method in causal linkage recovery and has been applied to various fields, such as economics and neuroscience. While the conventional Granger causality model is capable of identifying symmetrical causal relationships among variables, it is the asymmetric Granger causality that provides a more comprehensive perspective of the short- and long-term interactions between variables, which is of greater value for empirical study. Traditional vector autoregressive models lack the ability to explore multiscale information flow and are affected by the moving average component. Therefore, by combining the wavelet-based approach and state space model, we propose a new Granger causality analysis method to overcome the inherent limitation of vector autoregressive models and extend to multiscale causality exploration. Two simulations were conducted to compare the proposed approach to an existing wavelet-based method, and five evaluation indicators were utilized. The results indicate that the proposed method efficiently identifies the accurate asymmetric causalities at varying scales, while improving accuracy and reducing bias as compared to the current wavelet-based method. In conclusion, the combination of the wavelet approach and state space method enhances the multiscale causality detecting capability and can potentially contribute to multiscale Granger causality research.

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Journal articles on the topic 'Granger causality'

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