Relevant bibliographies by topics / Granger causality

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Granger causality'

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

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Journal articles on the topic "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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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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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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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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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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Dissertations / Theses on the topic "Granger causality"

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AZEVEDO, RONALDO. "GRANGER CAUSALITY IN TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8782@1.

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REDE FERROVIÁRIA FEDERAL SA
Neste trabalho fazemos uma revisita à causalidade no sentido de Granger aplicada às Séries Temporais bivariadas no domínio do tempo e da freqüência. Um programa computacional foi escrito usando a linguagem Pascal para, testando casos reais e simulados, construir modelos de causalidade/feedback, que são então analisados no ambiente espectral, com ênfase maior à discussão da coerência e da fase de causalidade.
In this work causality in the sense defined by Granger is revisited. Applications to bivariante temporal systems in time domain and frequency-domain were analysed, using a computer program written in Pascal. After this, spectral methods were developed, with special emphasis on phase and causality-coerence.
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Zou, Cunlu. "Applications of Granger causality to biological data." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/35694/.

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In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. In literature, there are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. To apply the four different approaches to the same problem, a key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results. In this thesis, I provided an answer by focusing on a systematic and computationally intensive comparison between the two common approaches which are dynamic Bayesian network inference and Granger causality. The comparison was carried out on both synthesized and experimental data. It is concluded that the dynamic Bayesian network inference performs better than the Granger causality approach, when the data size is short; otherwise the Granger causality approach is better. Since the Granger causality approach is able to detect weak interactions when the time series are long enough, I then focused on applying Granger causality approach on real experimental data both in the time and frequency domain and in local and global networks. For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. In addition to gene and protein data, Granger causality approach was also applied on Local Field Potential (LFP) data. Here we have combined multiarray electrophysiological recordings of local field potentials in both right inferior temporal (rIT) and left IT (lIT) and right anterior cingulate (rAC) cortices in sheep with Granger causality to investigate how anaesthesia alters processing during resting state and exposure to pictures of faces. Results from both the time and frequency domain analyses show that loss of consciousness during anaesthesia is associated with a reduction/disruption of feed forward open-loop cortico-cortical connections and a corresponding increase in shorter-distance closed loop ones.
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Stokes, Patrick A. "Fundamental problems in Granger causality analysis of neuroscience data." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97828.

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Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-115).
Granger causality methods analyze the flow of information between time series. The Geweke measure of Granger causality (GG-causality) has been widely applied in neuroscience because its frequency-domain and conditional forms appear well-suited to highly-multivariate oscillatory data. In this work, I analyze the statistical and structural properties of GG-causality in the context of neuroscience data analysis. 1. I analyze simulated examples and derive analytical expressions to demonstrate how computational problems arise in current methods of estimating conditional GG-causality. I show that the use of separate full and reduced models in the computation leads to either large biases or large uncertainties in the causality estimates, and high sensitivity to uncertainties in model parameter estimates, producing spurious peaks, valleys, and even negative values in the frequency domain. 2. I formulate a method of correctly computing GG-causality that resolves the above computational problems. 3. I analyze how generative system properties and frequency structure map into GG-causality to demonstrate deeper conceptual pitfalls: (a) I use simulated examples and derive analytical expressions to show that GG-causality is independent of the receiver dynamics, particularly the magnitude of response, which is counter-intuitive to physical notions of causality. (b) Overall, GG-causality combines transmitter and channel dynamics in a way that cannot be disentangled without evaluating the component dynamics of the full model estimate. 4. I discuss relevant concepts from causality analyses in other fields to better place causality analysis in a modeling and system identification framework. The computational uncertainties in GG-causality estimates make the interpretation of frequency-domain structure highly problematic. Even if these computational issues are overcome, correct interpretation of the GG-causality values is still challenging and could be easily misinterpreted without careful consideration of the component dynamics of the full model estimate. Through this work, I provide conceptual clarification of GG-causality and place it in the broader framework of modeling and system analysis, which may enable investigators to better assess the utility and interpretation of such methods.
by Patrick A. Stokes.
Ph. D.
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Nasseef, Md Taufiq. "Measuring directed functional connectivity in mouse fMRI networks using Granger Causality." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/368149.

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Resting-state functional magnetic resonance imaging (rsfMRI) of the mouse brain has revealed the presence of robust functional connectivity networks, including an antero-posterior system reminiscent of the human default network (DMN) and correlations between anterior insular and cingulate cortices recapitulating features of the human “salience network†. However, rsfMRI networks are typically identified using symmetric measurements of correlation that do not provide a description of directional information flow within individual network nodes. Recent progress has allowed the measure of directed maps of functional connectivity in the human brain, providing a novel interpretative dimension that could advance our understanding of the brains’ functional organization. Here, we used Granger Causality (GC), a measure of directed causation, to investigate the direction of information flow within mouse rsfMRI networks characterized by unidirectional (i.e. frontal-hippocampal) as well as reciprocal (e.g. DMN) underlying connectional architecture. We observed robust hippocampal-prefrontal dominant connectivity along the direction of projecting ventro-subicular neurons both at single subject and population level. Analysis of key DMN nodes revealed the presence of directed functional connectivity from temporal associative cortical regions to prefrontal and retrosplenial cortex, reminiscent of directional connectivity patterns described for the human DMN. We also found robust directional connectivity from insular to prefrontal areas. In a separate study, we reproduced the same directional connectivity fingerprints and showed that mice recapitulating a mutation associated to autism spectrum disorder exhibited reduced or altered directional connectivity. Collectively, our results document converging directional connectivity towards retrosplenial and prefrontal cortical areas consistent with higher integrative functions subserved by these regions, and provide a first description of directional topology in resting-state connectivity networks that complements ongoing research in the macroscale organization of the mouse brain.
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Nasseef, Md Taufiq. "Measuring directed functional connectivity in mouse fMRI networks using Granger Causality." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1582/1/Taufiq_thesis_CiMeC_UniTN.pdf.

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Resting-state functional magnetic resonance imaging (rsfMRI) of the mouse brain has revealed the presence of robust functional connectivity networks, including an antero-posterior system reminiscent of the human default network (DMN) and correlations between anterior insular and cingulate cortices recapitulating features of the human “salience network”. However, rsfMRI networks are typically identified using symmetric measurements of correlation that do not provide a description of directional information flow within individual network nodes. Recent progress has allowed the measure of directed maps of functional connectivity in the human brain, providing a novel interpretative dimension that could advance our understanding of the brains’ functional organization. Here, we used Granger Causality (GC), a measure of directed causation, to investigate the direction of information flow within mouse rsfMRI networks characterized by unidirectional (i.e. frontal-hippocampal) as well as reciprocal (e.g. DMN) underlying connectional architecture. We observed robust hippocampal-prefrontal dominant connectivity along the direction of projecting ventro-subicular neurons both at single subject and population level. Analysis of key DMN nodes revealed the presence of directed functional connectivity from temporal associative cortical regions to prefrontal and retrosplenial cortex, reminiscent of directional connectivity patterns described for the human DMN. We also found robust directional connectivity from insular to prefrontal areas. In a separate study, we reproduced the same directional connectivity fingerprints and showed that mice recapitulating a mutation associated to autism spectrum disorder exhibited reduced or altered directional connectivity. Collectively, our results document converging directional connectivity towards retrosplenial and prefrontal cortical areas consistent with higher integrative functions subserved by these regions, and provide a first description of directional topology in resting-state connectivity networks that complements ongoing research in the macroscale organization of the mouse brain.
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Salata, Andrea <1989&gt. "Differenze topologiche nei Granger Causality Networks: Esempi dal mercato dei CDS." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/5899.

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La tesi affronta sotto molteplici punti di vista il tema dei network costruiti sulla base di relazioni di causalità di Granger, applicate ai credit default swaps scambiati nel mercato tra banche e assicurazioni e stati. Affronta quindi i temi di cos'è la causalità di Granger e come sia possibile dedurne il network partendo dai dati dei CDS, per farne infine anche un confronto con il network ottenuto utilizzando i dati del fair value CDS per poter individuare le differenze topologiche tra i due metodi di indagine del network.
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Mbeleke, Paul Wuakoh. "The monetary sector in Cameroon money demand and causality analysis." Thesis, University of Salford, 1997. http://usir.salford.ac.uk/26806/.

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This thesis investigates the monetary sector in Cameroon within an open economy framework. Two main hypotheses: money demand and Granger-causality are investigated. The data used are found to be non-stationary. Consequently, the money demand relationship is tested for the null hypothesis that it is spurious or not co-integrated. This is rejected in all the models put forward. The models are estimated and found to exhibit elasticities that are not unusual. Price homogeneity is found to be data incompatible. Income elasticities are generally found to be significantly less than unity suggesting economies of scale in money holdings. Corresponding dynamic models in the form of error correction are constructed using the familiar general to specific methodology and generally found to exhibit desirable statistical properties. Model preference is in terms of the narrow Ml definition of money with explanatory variables which include a foreign interest rate. For Granger-causality, the non-stationary data are transformed into stationarity where the null hypothesis of noncausality is tested in bivariate and multivariate contexts. Lag length selection is by the Final Prediction Error statistic. Results are mixed but two appear striking: domestic money and prices are found to be independent while domestic prices are Granger-caused by foreign variables but not by domestic ones.
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Guo, Yuanxiang. "Chinese wheat price analysis - with application of cointegration and Granger causality test." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52978.

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Traditional demonstration of price fluctuation in the wheat market, by the theory of supply and demand is not comprehensive enough. With limited understanding of macroeconomic effects on the wheat market, accurate prediction of wheat price is impossible. Given the Chinese self—sustainable food policy, grain imports is a sensitive topic which may incur fierce argument. In this paper, however, I emphasize effect of exchange rate on nominal wheat price. By application of the cointegration theory, CPI shows slight negative correlation with nominal wheat price, yet GDP and population move in the same direction as the wheat price. The cointegration study of exchange rate implies, with appreciating Chinese RMB, domestic buyers incline to purchase wheat from the cheaper foreign market. According to the Granger causality test, the whole package of variables suggests significant causal relation with the wheat price.
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SUN, FEI. "Analysis to China's Urban and Rural CPI Data." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175796.

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Murakami, Patricia Nagami. "Causalidade Granger em medidas de risco." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14072011-221932/.

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Esse trabalho apresenta um estudo da causalidade de Granger em Risco bivariado aplicado a séries temporais financeiras. Os eventos de risco, no caso de séries financeiras, estão relacionados com a avaliação do Valor em Risco das posições em ativos. Para isso, os modelos CaViaR, que fazem parte do grupo de modelos de Regressão Quantílica, foram utilizado para identificação desses eventos. Foram expostos os conceitos principais envolvidos da modelagem, assim como as definições necessárias para entendê-las. Através da análise da causalide de Granger em risco entre duas séries, podemos investigar se uma delas é capaz de prever a ocorrência de um valor extremo da outra. Foi realizada a análise de causalidade de Granger usual somente para como comparativo.
Quantile Regression, Value at Risk, CAViaR Model, Granger Causality, Granger Causality in Risk
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Books on the topic "Granger causality"

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Yi, Wen. Granger causality and equilibrium business cycle theory. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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Reuveny, Rafael. International trade, political conflict/cooperation, and Granger causality. Bloomington, Indiana: Indiana University, Graduate School of Business, 1994.

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Bianchi, Marco. Granger causality in the presence of structural changes. London: Bank of England, 1995.

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Reuveny, Rafael. Disaggregated bilateral trade political conflict/cooperation and Granger causality. Bloomington, Indiana: Indiana University, Graduate School of Business, 1996.

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Halbert, White, Engle R. F, and Granger, C. W. J. 1934-, eds. Cointegration, causality, and forecasting: A festschrift in honour of Clive W.J. Granger. Oxford: Oxford University Press, 1999.

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Lyons, Susanna E. Cointegration and Granger-causality testing of hypotheses on supply-leading and demand-following finance. Cardiff: Cardiff Business School, Financial and Banking Economics Research Group, 1993.

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Spanos, Aris. On the statistical implications of modelling the error term: Common factors, Granger-causality and unit roots. London: Birkbeck College, Dept. of Economics, 1987.

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Abhyankar, Abhay. Linear and nonlinear Granger causality: Evidence from the FT-SE 100 stock index futures and cash markets. Stirling: University of Stirling, Dept. of Accountancy and Finance, 1994.

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Zhongguo jin rong fa zhan yu jing ji zeng chang de yin guo fang xiang xing shi zheng yan jiu: 1995-2004 = An empirical study on the direction of granger causality between financial development and economic growth in China. Beijing Shi: Jing ji ke xue chu ban she, 2008.

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Zhongguo jin rong fa zhan yu jing ji zeng chang de yin guo fang xiang xing shi zheng yan jiu: 1995-2004 = An empirical study on the direction of granger causality between financial development and economic growth in China. Beijing Shi: Jing ji ke xue chu ban she, 2008.

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Book chapters on the topic "Granger causality"

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Kirchgässner, Gebhard, Jürgen Wolters, and Uwe Hassler. "Granger Causality." In Introduction to Modern Time Series Analysis, 95–125. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33436-8_3.

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Kirchgässner, Gebhard, and Jürgen Wolters. "Granger Causality." In Introduction to Modern Time Series Analysis, 93–123. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73291-4_3.

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Deistler, Manfred, and Wolfgang Scherrer. "Granger Causality." In Time Series Models, 167–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1_9.

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Kuersteiner, G. M. "Granger–Sims Causality." In The New Palgrave Dictionary of Economics, 1–13. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2095-1.

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Kuersteiner, G. M. "Granger–Sims Causality." In The New Palgrave Dictionary of Economics, 5413–25. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2095.

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Kuersteiner, G. M. "Granger-Sims causality." In Macroeconometrics and Time Series Analysis, 119–34. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_14.

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Koller, Ingrid, Claus H. Carstensen, Wolfgang Wiedermann, and Alexander von Eye. "Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models." In Statistics and Causality, 231–48. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118947074.ch10.

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Guo, Shuixia, Christophe Ladroue, and Jianfeng Feng. "Granger Causality: Theory and Applications." In Frontiers in Computational and Systems Biology, 83–111. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-196-7_5.

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Li, Xiang, Kaiming Li, Lei Guo, Chulwoo Lim, and Tianming Liu. "Fiber-Centered Granger Causality Analysis." In Lecture Notes in Computer Science, 251–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23629-7_31.

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Behzadi, Sahar, Kateřina Hlaváčková-Schindler, and Claudia Plant. "Granger Causality for Heterogeneous Processes." In Advances in Knowledge Discovery and Data Mining, 463–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16142-2_36.

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Conference papers on the topic "Granger causality"

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Park, Il, and Jose C. Principe. "Correntropy based Granger causality." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518432.

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Jia, Xinxin, Sanqing Hu, Jianhai Zhang, and Wanzeng Kong. "Blockwise Granger causality and blockwise new causality." In 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2015. http://dx.doi.org/10.1109/icaci.2015.7184744.

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Chvostekova, Martina. "Granger Causality Inference and Time Reversal." In 2019 12th International Conference on Measurement. IEEE, 2019. http://dx.doi.org/10.23919/measurement47340.2019.8779895.

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Gautama, T., and M. M. Van Hulle. "Surrogate-based test for Granger causality." In 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718). IEEE, 2003. http://dx.doi.org/10.1109/nnsp.2003.1318079.

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Stramaglia, S., L. Angelini, J. M. Cortes, and D. Marinazzo. "Synergy, redundancy and unnormalized Granger causality." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319280.

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Stramaglia, S., L. Angelini, M. Pellicoro, and D. Marinazzo. "Nonlinear granger causality for brain connectivity." In 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2011. http://dx.doi.org/10.1109/memea.2011.5966694.

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Amblard, P. O., R. Vincent, O. J. J. Michel, and C. Richard. "Kernelizing Geweke's measures of granger causality." In 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349710.

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Balazia, Michal, Katerina Hlavackova-Schindler, Petr Sojka, and Claudia Plant. "Interpretable Gait Recognition by Granger Causality." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956624.

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Qiu, Huida, Yan Liu, Niranjan A. Subrahmanya, and Weichang Li. "Granger Causality for Time-Series Anomaly Detection." In 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.73.

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Su, Jing-Ru, Jian-Guo Wang, Long-Fei Deng, Yuan Yao, and Jian-Long Liu. "Granger Causality Detection Based on Neural Network." In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2020. http://dx.doi.org/10.1109/ddcls49620.2020.9275129.

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Reports on the topic "Granger causality"

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Wen, Yi. Granger Causality and Equilibrium Business Cycle Theory. Federal Reserve Bank of St. Louis, 2005. http://dx.doi.org/10.20955/wp.2005.038.

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Buiter, Willem. Granger-Causality and Policy Ineffectiveness: A Rejoinder. Cambridge, MA: National Bureau of Economic Research, October 1986. http://dx.doi.org/10.3386/t0061.

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Hafer, R. W., and Scott E. Hein. Federal Government Debt and Inflation: Evidence from Granger Causality Tests. Federal Reserve Bank of St. Louis, 1986. http://dx.doi.org/10.20955/wp.1986.003.

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Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, November 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features. Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events. In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution. With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics. The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.
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Mbaye, Safiétou, Rémi Kouabenan, and Philippe Sarnin. L'explication naïve et la perception des risques comme des voies pour améliorer les pratiques de REX: des études dans l'industrie chimique et l'industrie nucléaire. Fondation pour une culture de sécurité industrielle, September 2009. http://dx.doi.org/10.57071/311rex.

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L’analyse de l’accident soulève de nombreux enjeux dont le poids préfigure également des biais possibles dans l’explication causale et laisse apparaître que l’explication de l’accident peut difficilement être neutre. En l’occurrence, les difficultés rencontrées dans la conduite du REX relèvent en grande partie de conflits qui surviennent lors des analyses d’accidents. Ce document présente quatre études de terrain qui visaient à comprendre l’origine des conflits entre les acteurs du REX lors des analyses d’accidents et à mieux appréhender le rôle de la perception des risques et du climat de sécurité dans la motivation des acteurs à s’impliquer davantage dans les pratiques de REX. Les études sont conduites dans l’industrie chimique et l’industrie nucléaire et portent sur plus de 1000 agents de tous niveaux hiérarchiques (cadres, agents de maîtrise, techniciens, ouvriers) et tous domaines d’activité (prévention, production, maintenance, qualité). La méthodologie repose sur des entretiens, des observations de comité REX, une expérimentation et l’administration de deux questionnaires. Il en ressort que les démarches de REX sont davantage subies qu’elles ne sont portées par les responsables du traitement des accidents en raison des coûts qu’elles engendrent en temps et en énergie, mais aussi à cause du manque d’opérationnalité des règles de traitement des accidents. Il s’avère ensuite que l’absence de dialogue autour des causes des accidents entretient des doutes sur la crédibilité du REX auprès des opérateurs. Mais avant tout, il est clairement établi que la peur d’endosser la responsabilité de l’accident détermine fortement l’explication de l’accident qui devient notamment très défensive. Par exemple, les cadres et les ouvriers se renvoient systématiquement la causalité des accidents: plus ils se sentent menacés par l’analyse d’accident, plus ils en attribuent la survenue à des facteurs internes à l’autre groupe hiérarchique. Les études révèlent également comment les croyances sur les risques conduisent les individus à être plus attentifs au REX sur les accidents directement liés au cœur de métier de leur industrie et moins attentifs au REX sur les accidents de la vie courante, pourtant plus nombreux et plus graves. Enfin, nous montrons en quoi un bon climat de sécurité peut favoriser l’implication des acteurs dans les pratiques de REX.
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