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Journal articles on the topic 'Granger causality'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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 (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

Chao, John, Valentina Corradi, and Norman R. Swanson. "OUT-OF-SAMPLE TESTS FOR GRANGER CAUSALITY." Macroeconomic Dynamics 5, no. 4 (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

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7

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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8

Elsner, James B. "Granger causality and Atlantic hurricanes." Tellus A: Dynamic Meteorology and Oceanography 59, no. 4 (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 (2015): 1157–66. http://dx.doi.org/10.1177/1536867x1501500411.

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10

Angelini, Leonardo, Mario Pellicoro, and Sebastiano Stramaglia. "Granger causality for circular variables." Physics Letters A 373, no. 29 (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 (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 (2011): 330–38. http://dx.doi.org/10.1016/j.neuroimage.2010.01.099.

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13

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

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14

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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15

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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16

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

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<strong>Abstract</strong> <em>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

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17

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 (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-ca

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18

FUJITA, ANDRÉ, JOÃO RICARDO SATO, KANAME KOJIMA, et al. "IDENTIFICATION OF GRANGER CAUSALITY BETWEEN GENE SETS." Journal of Bioinformatics and Computational Biology 08, no. 04 (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 l

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19

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 (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.

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20

Kwon, Tae Yeon. "Causality Change in Life Insurance Demand: Focusing on Financial Market and Insurance Provider Factors." Korean Data Analysis Society 25, no. 1 (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 cau

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21

Qian, Jing, Wenpo Yao, Dengxuan Bai, et al. "Depressed MEG causality analysis based on polynomial kernel Granger causality." AIP Advances 13, no. 3 (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

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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 (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 (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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24

Hu, Sanqing, Yu Cao, Jianhai Zhang, et al. "More discussions for granger causality and new causality measures." Cognitive Neurodynamics 6, no. 1 (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 (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 als

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26

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 Ca

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27

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 per

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28

Lam, Weng Siew, Weng Hoe Lam, Saiful Hafizah Jaaman, and Pei Fun Lee. "Bibliometric Analysis of Granger Causality Studies." Entropy 25, no. 4 (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 tr

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29

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 (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

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30

Barika, Barika, Armelly Armelly, and Benardin Benardin. "TINJAUAN KAUSALITAS INDIKATOR MAKROEKONOMI DI PROVINSI BENGKULU." Convergence: The Journal of Economic Development 2, no. 2 (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 invest

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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 (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 direction

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32

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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33

Sobieraj, Marcin, and Piotr Setny. "Granger Causality Analysis of Chignolin Folding." Journal of Chemical Theory and Computation 18, no. 3 (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 (2002): 105–32. http://dx.doi.org/10.5109/13513.

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35

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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36

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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37

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

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38

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

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39

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

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40

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

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41

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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42

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

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43

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

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44

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

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45

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

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46

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

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47

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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48

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 (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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49

Lahmiri, Salim. "Causality Between Brent and West Texas Intermediate: The Effects of COVID-19 Pandemic and Russia–Ukraine War." Commodities 4, no. 1 (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–Ukr

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50

Zhang, Qiyi, Chuanlin Zhang, and Shuangqin Cheng. "Wavelet Multiscale Granger Causality Analysis Based on State Space Models." Symmetry 15, no. 6 (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 co

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