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Journal articles on the topic 'Granger causality'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 May 2024

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1

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

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2

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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3

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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5

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

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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7

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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8

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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9

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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10

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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11

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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12

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

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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30

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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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

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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41

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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42

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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43

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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44

Chvosteková, Martina, Jozef Jakubík, and Anna Krakovská. "Granger Causality on forward and Reversed Time Series." Entropy 23, no. 4 (March 30, 2021): 409. http://dx.doi.org/10.3390/e23040409.

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In this study, the information flow time arrow is investigated for stochastic data defined by vector autoregressive models. The time series are analyzed forward and backward by different Granger causality detection methods. Besides the normal distribution, which is usually required for the validity of Granger causality analysis, several other distributions of predictive errors are considered. A clear effect of a change in the order of cause and effect on the time-reversed series of unidirectionally connected variables was detected with standard Granger causality test (GC), when the product of the connection strength and the ratio of the predictive errors of the driver and the recipient was below a certain level, otherwise bidirectional causal connection was detected. On the other hand, opposite causal link was detected unconditionally by the methods based on the time reversal testing, but they were not able to detect correct bidirectional connection. The usefulness of the backward analysis is manifested in cases where falsely detected unidirectional connections can be rejected by applying the result obtained after the time reversal, and in cases of uncorrelated causally independent variables, where the absence of a causal link detected by GC on the original series should be confirmed on the time-reversed series.
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45

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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46

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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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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47

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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48

Arif, Abubakar, and R. Rosiyana Dewi. "ANALISIS KAUSALITAS ANTARA PENERIMAAN PAJAK DENGAN TINGKAT KEGIATAN EKONOMI INDONESIA DENGAN PENDEKATAN GRANGER CAUSALITY." JURNAL INFORMASI, PERPAJAKAN, AKUNTANSI, DAN KEUANGAN PUBLIK 8, no. 1 (May 7, 2019): 1. http://dx.doi.org/10.25105/jipak.v8i1.4502.

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<p>The purpose of this study is to analysis the causality between tax revenue and economy activity in Indonesia period 1990-2010. The design of this research used time series analysis model with Granger Causality Model with the step analusis are (1) determine long of lag to know how long tax revenue and economy activity influence, (2) determine causality test to proof there are causality or there are correlation one direction between tax revenue and economy activity, (3) analyze the power of correlation or influence between tax revenue and economy activity after the result of causali8ty test was reach. The analysis used in this research was time serieswith annual data,starting 1990 too 2010. Tax revenue and economy activity with proxy product domestic bruto are valuables used in this study. The result of this study are not find causality between tax revenue and economy activity in Indonesia but one direction correlation when tax revenue influence economy activity.By the regression analysis can proff that tax revenue influence significant positive to economy activity with multiplier effect less than 1.</p>
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49

Rodrigues, João, and Alexandre Andrade. "Instantaneous Granger Causality with the Hilbert-Huang Transform." ISRN Signal Processing 2013 (February 20, 2013): 1–9. http://dx.doi.org/10.1155/2013/374064.

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Current measures of causality and temporal precedence have limited frequency and time resolution and therefore may not be viable in the detection of short periods of causality in specific frequencies. In addition, the presence of nonstationarities hinders the causality estimation of current techniques as they are based on Fourier transforms or autoregressive model estimation. In this work we present a combination of techniques to measure causality and temporal precedence between stationary and nonstationary time series, that is sensitive to frequency-specific short episodes of causality. This methodology provides a highly informative time-frequency representation of causality with existing causality measures. This is done by decomposing each time series into intrinsic oscillatory modes with an empirical mode decomposition algorithm and, subsequently, calculating their complex Hilbert spectrum. At each time point the cross-spectrum is calculated between time series and used to measure coherency and compute the transfer function and error covariance matrices using the Wilson-Burg method for spectral factorization. The imaginary part of coherency can then be computed as well as several Granger causality measures in the previous matrices. This work covers the most important theoretical background of these techniques and tries to prove the usefulness of this new approach while pointing out some of its qualities and drawbacks.
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50

Santi, Nindya Eka, Aisyah Jumiarti, and Fivien Muslihatinningsih. "Analisis Kausalitas Pengeluaran Pemerintah, Investasi, dan Tenaga Kerja Terhadap Produk Domestik Regional Bruto SWP Jember dan Sekitarnya." e-Journal Ekonomi Bisnis dan Akuntansi 5, no. 1 (May 22, 2018): 6. http://dx.doi.org/10.19184/ejeba.v5i1.7707.

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RGDP is the total of all goods and services value which produced by all economics unit within a region. This study aims to determine the causality’s direction between government budget and RGDP, investment and RGDP, labor and RGDP, using panel data on RDU Jember and its regional area during 2000– 2014. The Granger causality test is used to identify the direction of the relationship between the variable between government budget and RGDP, investment and RGDP, labor and RGDP. The result of this study showed that there is a causal relationship between variables.Keywords: Granger causality, RGDP, government budget, investment, and labor
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