Relevant bibliographies by topics / Conditional Granger causality (CGC)

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Academic literature on the topic 'Conditional Granger causality (CGC)'

Author: Grafiati
Published: 6 September 2023
Last updated: 7 September 2023

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Journal articles on the topic "Conditional Granger causality (CGC)"

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Vidhusha S and Kavitha Anandan. "Inter-hemispherical Investigations on the Functional Connectivity of Autistic Resting State fMRI." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 2 (April 2016): 95–108. http://dx.doi.org/10.4018/ijcini.2016040105.

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Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task i.e., “rest.” Adults with ASD have been found to show weaker connectivity relative to controls. This work focuses on analyzing the brain activation for autistic subjects, measured by fMRI during rest, relative to the control group using interhemispherical analysis. Though both groups activated similarly in cortical areas, indications of under connectivity were exhibited by the autistic group measured by Granger Causality and Conditional Granger Causality. Results show that as connectivity decreases, GC and CGC values also get decreased. The left hemisphere exhibits depreciation in the connectivity in comparison to that of right hemisphere for the autistic individuals whose GC and CGC values keeps decreasing in the left hemisphere seed regions. Finally, the results provide an approach for analyzing the cortical underconnectivity, in clinical relevance for diagnosing autism in children.
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Wang, Yu Qing, Hua Fu Chen, and Ling Zeng. "Evaluation of Causal Influences in Model of Motor Control in Left Hands Movement-Readiness State." Applied Mechanics and Materials 195-196 (August 2012): 418–23. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.418.

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The previous research revealed some functional coupling among nodes in model of motor control in human brain, which described nondirectional synchronous actions among these nodes during movement-readiness state. However, causal relationships among these nodes were still lack, which represented some directional interactions among these nodes in movement-readiness state. In the present study, we used functional magnetic resonance imaging (fMRI) and conditional Granger causality (CGC) method to investigate the interactions in model of motor control in movement-readiness state. Our result showed that upper precuneus and cingulate motor area revealed net causal influences with contralateral supplementary motor areas and contralateral caudate nucleus during the left hands movement-readiness state. Moreover, the results of Out-In degrees indicated that bilateral primary sensorimotor areas revealed competitive relationship during left hands movement-readiness.
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WONG, HOCK TSEN. "REAL EXCHANGE RATE RETURNS AND REAL STOCK PRICE RETURNS IN THE STOCK MARKET OF MALAYSIA." Singapore Economic Review 64, no. 05 (December 12, 2016): 1319–49. http://dx.doi.org/10.1142/s0217590816500387.

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This study examines the relationships between real exchange rate returns and real stock price returns in the stock market of Malaysia. The Kwiatkowski, Phillips, Schmidt and Shin (KPSS) and Dickey and Fuller (DF) unit root test statistics show that all the variables examined are found to be stationary in the first differences. The constant conditional correlation (CCC)-multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model shows that real exchange rate return of Malaysian ringgit against the United States dollar (RM/USD) and real stock price return of Kuala Lumpur Composite Index (KLCI) are found to be negative and significantly correlated. However, there is insignificant correlation between real exchange rate return of Malaysian ringgit against Japanese Yen (RM/¥) and real stock price return of KLCI. Moreover, the CCC-MGARCH models show that real exchange rate returns and real stock price returns of some stocks are found to be significantly correlated. The KPSS unit root test statistics show that the time invariant conditional variances of real exchange rate returns and real stock price returns are mostly found to be stationary in the levels. There is no evidence of Granger causality between the time invariant conditional variances of real exchange rate returns and real stock price return of KLCI but some evidence of Granger causality between the time invariant conditional variances of real exchange rate returns and real stock price returns. There is a link between the exchange rate market and the stock market in Malaysia but not every real stock price return is significantly linked with real exchange rate return.
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Wang, Li, Jingna Zhang, Ye Zhang, Rubing Yan, Hongliang Liu, and Mingguo Qiu. "Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3870863.

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Aims.Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients.Methods.Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery.Results.Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere.Conclusions.The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function.
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Ferro, Demetrio, Jochem van Kempen, Michael Boyd, Stefano Panzeri, and Alexander Thiele. "Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention." Proceedings of the National Academy of Sciences 118, no. 12 (March 15, 2021): e2022097118. http://dx.doi.org/10.1073/pnas.2022097118.

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Achieving behavioral goals requires integration of sensory and cognitive information across cortical laminae and cortical regions. How this computation is performed remains unknown. Using local field potential recordings and spectrally resolved conditional Granger causality (cGC) analysis, we mapped visual information flow, and its attentional modulation, between cortical layers within and between macaque brain areas V1 and V4. Stimulus-induced interlaminar information flow within V1 dominated upwardly, channeling information toward supragranular corticocortical output layers. Within V4, information flow dominated from granular to supragranular layers, but interactions between supragranular and infragranular layers dominated downwardly. Low-frequency across-area communication was stronger from V4 to V1, with little layer specificity. Gamma-band communication was stronger in the feedforward V1-to-V4 direction. Attention to the receptive field of V1 decreased communication between all V1 layers, except for granular-to-supragranular layer interactions. Communication within V4, and from V1 to V4, increased with attention across all frequencies. While communication from V4 to V1 was stronger in lower-frequency bands (4 to 25 Hz), attention modulated cGCs from V4 to V1 across all investigated frequencies. Our data show that top-down cognitive processes result in reduced communication within cortical areas, increased feedforward communication across all frequency bands, and increased gamma-band feedback communication.
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Zhu, Hongmin. "Real-Time Prognostics of Engineered Systems under Time Varying External Conditions Based on the COX PHM and VARX Hybrid Approach." Sensors 21, no. 5 (March 2, 2021): 1712. http://dx.doi.org/10.3390/s21051712.

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In spite of the development of the Prognostics and Health Management (PHM) during past decades, the reliability prognostics of engineered systems under time-varying external conditions still remains a challenge in such a field. When considering the challenge mentioned above, a hybrid method for predicting the reliability index and the Remaining Useful Life (RUL) of engineered systems under time-varying external conditions is proposed in this paper. The proposed method is competent in reflecting the influence of time-varying external conditions on the degradation behaviour of engineered systems. Based on a subset of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset as case studies, the Cox Proportional Hazards Model (Cox PHM) with time-varying covariates is utilised to generate the reliability indices of individual turbofan units. Afterwards, a Vector Autoregressive model with Exogenous variables (VARX) combined with pairwise Conditional Granger Causality (CGC) tests for sensor selections is defined to model the time-varying influence of sensor signals on the reliability indices of different units that have been previously generated by the Cox PHM with time-varying covariates. During the reliability prediction, the Fourier Grey Model (FGM) is employed with the time series models for long-term forecasting of the external conditions. The results show that the method that is proposed in this paper is competent for the RUL prediction as compared with baseline approaches.
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Singh, Amanjot, and Manjit Singh. "How linkages fuel dependent economic policy initiatives." International Journal of Law and Management 59, no. 2 (March 13, 2017): 303–18. http://dx.doi.org/10.1108/ijlma-01-2016-0007.

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Purpose With the globalization and liberalization in terms of increasing financial flows across the countries, the policy makers around the world are not independent in the context of monetary and fiscal policy initiatives. In this regard, this paper aims to attempt to quantify and capture long run, short run as well as time-varying linkages among the two financial stress indices, namely, Kansas City Financial Stress Index (KCFSI) and Indian Financial Stress Index (IFSI) across the monthly period (2004 to 2014). Design/methodology/approach Owing to the non-existence of a standardized financial stress index with regards to the Indian financial system, the study has developed an index/stress indicator using principal component analysis. Furthermore, to comprehend the linkages, the study uses bivariate Johansen cointegration model, vector error correction model, impulse response functions (IRF), variance decomposition analysis (VDA), Toda-Yamamoto’s Granger causality test and, finally, bivariate generalized autoregressive conditional heteroskedastic (BVGARCH) (1,1) model under constant conditional correlation (CCC) framework. Findings The results report a stochastic trend among the two indices wherein the US financial system acts as a source of a shock causing disequilibrium in the long run co-movement. About 40 per cent of the adjustments take place in one month and rest in the coming months. Both the IRF and VDA report a greater degree impact of the US financial stress on the Indian financial system. Moreover, there is a uni-directional short run causality running from the stress in the US financial system to the Indian financial stress. Furthermore, the co-movement between the US and Indian financial stress reached to its maximum significant level during the sub-prime crisis even confirmed by the Markov switching model results. Practical implications Overall, the results provide an insight to the financial market investors both domestic as well as international in their act of risk management. The financial stress prevailing in an economy further has an impact on different economic factors like foreign exchange rates, interest rates, yield curves, equity market returns and volatility. So, the empirical results support strong implications for the Indian policy makers as well as investors in the Indian financial markets. Originality/value The present study contributes to the literature in three senses. First, the study considers indices reflecting financial stress in the Indian as well as US financial system. Second, the study captures long run as well as short run linkages among the financial stress indices relating to a developed and an emerging market. Finally, the study uses CCC-BVGARCH (1,1) model to account for the time-varying co-movement among the financial stress indices. This helps in comprehending time-varying nature of the co-movement of the stress in the financial system prevalent in the respective markets.
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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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Lu, Xun, Liangjun Su, and Halbert White. "GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA." Econometric Theory 33, no. 2 (March 17, 2016): 263–91. http://dx.doi.org/10.1017/s0266466616000086.

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Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also impliesG–causality. This justifies using tests ofG–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.
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Wang, Xia, and Yongmiao Hong. "CHARACTERISTIC FUNCTION BASED TESTING FOR CONDITIONAL INDEPENDENCE: A NONPARAMETRIC REGRESSION APPROACH." Econometric Theory 34, no. 4 (April 11, 2017): 815–49. http://dx.doi.org/10.1017/s026646661700010x.

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We propose a characteristic function based test for conditional independence, applicable to both cross-sectional and time series data. We also derive a class of derivative tests, which deliver model-free tests for such important hypotheses as omitted variables, Granger causality in various moments and conditional uncorrelatedness. The proposed tests have a convenient asymptotic null N (0, 1) distribution, and are asymptotically locally more powerful than a variety of related smoothed nonparametric tests in the literature. Unlike other smoothed nonparametric tests for conditional independence, we allow nonparametric estimators for both conditional joint and marginal characteristic functions to jointly determine the asymptotic distributions of the test statistics. Monte Carlo studies demonstrate excellent power of the tests against various alternatives. In an application to testing Granger causality, we document the existence of nonlinear relationships between money and output, which are missed by some existing tests.
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Dissertations / Theses on the topic "Conditional Granger causality (CGC)"

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Burda, Maike M. "Testing for causality with Wald tests under nonregular conditions." Doctoral thesis, [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=968852432.

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Mody, Sandeep K. "Computation of Sparse Representations of High Dimensional Time Series Data and Experimental Applications." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4295.

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Obtaining a sparse representation of high dimensional data is often the first step towards its further analysis. Conventional Vector Autoregressive (VAR) modelling methods applied to such data results in noisy, non-sparse solutions with a too many spurious Coefficients. Computing auxiliary quantities such as the Power Spectrum, Coherence and Granger causality (GC) from such non-sparse models is slow and gives wrong results. Thresholding the distorted values of these quantities as per some criterion, statistical or otherwise, does not alleviate the problem. We propose two sparse Vector Autoregressive (VAR) modelling methods that work well for high dimensional time series data, even when the number of time points is relatively low, by incorporating only statistically significant Coefficients. In numerical experiments using simulated data, our methods show consistently higher accuracy compared to other contemporary methods in recovering the true sparse model. The relative absence of spurious Coefficients in our models permits more accurate, stable and efficient evaluation of auxiliary quantities. Our VAR modelling methods are capable of computing Conditional Granger causality (CGC) in datasets consisting of tens of thousands of variables with a speed and accuracy that far exceeds the capabilities of existing methods. Using the Conditional Granger causality computed from our models as a proxy for the weight of the edges in a network, we use community detection algorithms to simultaneously obtain both local and global functional connectivity patterns and community structures in large networks. We also use our VAR modelling methods to predict time delays in many-variable systems. Using simulated data from non-linear delay differential equations, we compare our methods with commonly used delay prediction techniques and show that our methods yield more accurate results. We apply the above methods to the following real experimental data: 1. Analysis of data from the Human Connectome Project (HCP): fMRI data from the HCP database is used to compute sparse brain functional connectivity networks. The network and community structures obtained are consistent over independent recording sessions and show good spatial correspondence with known functional and anatomical regions of the brain. 2. Analysis of ADHD-200 data: fMRI data from children with ADHD (Attention Deficit Hyperactive Disorder) is used to compute sparse brain functional connectivity networks. Analysis of the network measures obtained provide new ways of differentiating between ADHD and typically developing children using global and node-level network measures. They also enable refinement of published results relating to the rFIC-ACC interaction in fMRI resting state data. 3. Time-delay prediction from LFP recordings: When applied to Local Field Potential (LFP) recordings from the rat and monkey, our methods predict consistent delays across a range of sampling frequencies. 4. Application to the Hela gene interaction dataset: The network obtained by applying our methods to this dataset yields results that are at least as good as those from a specialized method for analyzing gene interaction. This demonstrates that our methods can be applied to any time series data for which VAR modelling is valid. In addition to the above methods, we apply non-parametric Granger causality analysis (originally developed by A. Nedungadi, G. Rangarajan et al) to mixed point process and local field potential data. Extending the computations to Conditional GC and by increasing the efficiency of the original algorithm, we can compute the Conditional GC spectrum in systems consisting of hundreds of variables in a relatively short period. Further, combining this with VAR modelling provides an alternate faster route to compute the significance level of each element of the GC and CGC matrices. We use these techniques to analyze mixed spike train and LFP data from monkey electrocorticography (ECoG) recordings during a behavioral task. A preliminary interpretation of the results of the analysis is provided.
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Book chapters on the topic "Conditional Granger causality (CGC)"

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He, Yuqing, Bin Hao, Abdelkader Nasreddine Belkacem, Jiaxin Zhang, Penghai Li, Jun Liang, Changming Wang, and Chao Chen. "Brain Network Analysis of Hand Motor Execution and Imagery Based on Conditional Granger Causality." In Human Brain and Artificial Intelligence, 125–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8222-4_11.

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Chuang, Kai-Cheng, Sreekrishna Ramakrishnapillai, Lydia Bazzano, and Owen Carmichael. "Nonlinear Conditional Time-Varying Granger Causality of Task fMRI via Deep Stacking Networks and Adaptive Convolutional Kernels." In Lecture Notes in Computer Science, 271–81. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16431-6_26.

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Vidhusha, S., and A. Kavitha. "Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI." In Advances in Computational Intelligence and Robotics, 169–86. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3038-2.ch009.

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Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.
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Kundu, Amit. "The Asymmetric Volatility Spillover Effect of The Indian Stock Market to Sri Lanka-CSSE, Bangladesh-DSE." In Sustainable Roadmap Development Strategies in India: Paving the Way for a Better Future, 09–19. Lincoln University College, Malaysia, 2023. http://dx.doi.org/10.31674/book.2023srdsi002.

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This article focuses on the effect of spillover from the Indian stock market to the Sri Lankan and Bangladeshi stock exchanges. The major goal of this research is to determine whether investing in these two markets offers suitable prospects for diversification. The degree of economic interconnectedness between the economies of India and Asia is examined using Granger causality tests and dynamic conditional correlation (DCC)-MGARCH. India, Sri Lanka, and Bangladesh are not found to be causally related. Using DCC-MGARCH, it was found that while there was no short-term volatility spillover from India to Sri Lanka or Bangladesh, there was long-term volatility spillover from India to the other nations examined in this study. By taking into account the pattern of volatility transmission from the Indian stock market to the stock markets in Bangladesh and Sri Lanka, the study's findings may help market managers formulate regulations.
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Conference papers on the topic "Conditional Granger causality (CGC)"

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Zhou, Ben, Guanxue Yang, Zhaowei Wang, and Guanxiao Yang. "Identifying Topology of Complex Networked System Based on Block Orthogonal Matching Pursuit and Nonlinear Conditional Granger Causality." In 2022 China Automation Congress (CAC). IEEE, 2022. http://dx.doi.org/10.1109/cac57257.2022.10055616.

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Guorong Wu, Cuiping Xu, and Huafu Chen. "Investigate intracranial EEG with conditional granger causality and PCA." In 2010 International Conference of Medical Image Analysis and Clinical Application (MIACA). IEEE, 2010. http://dx.doi.org/10.1109/miaca.2010.5528282.

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Seth, Sohan, and Jose C. Principe. "A Test of Granger Non-causality Based on Nonparametric Conditional Independence." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.642.

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