Journal articles: '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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Stokes, P. A., M. Vangel, F. H. Lin, T. Raij, D. P. Nguyen, R. Barbieri, M. S. Hamalainen, E. N. Brown, and P. L. Purdon. "Dynamic Frequency-Domain Conditional Granger Causality Applied to Magnetoencephalography." NeuroImage 47 (July 2009): S148. http://dx.doi.org/10.1016/s1053-8119(09)71509-5.

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Xiao, Yanyang, Songting Li, and Douglas Zhou. "Representing conditional Granger causality by vector auto-regressive parameters." Communications in Mathematical Sciences 17, no. 5 (2019): 1353–86. http://dx.doi.org/10.4310/cms.2019.v17.n5.a9.

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Zhou, Zhenyu, Yonghong Chen, Mingzhou Ding, Paul Wright, Zuhong Lu, and Yijun Liu. "Analyzing brain networks with PCA and conditional Granger causality." Human Brain Mapping 30, no. 7 (July 2009): 2197–206. http://dx.doi.org/10.1002/hbm.20661.

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Marica, Vasile George, and Alexandra Horobet. "Conditional Granger Causality and Genetic Algorithms in VAR Model Selection." Symmetry 11, no. 8 (August 3, 2019): 1004. http://dx.doi.org/10.3390/sym11081004.

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Overcoming symmetry in combinatorial evolutionary algorithms is a challenge for existing niching methods. This research presents a genetic algorithm designed for the shrinkage of the coefficient matrix in vector autoregression (VAR) models, constructed on two pillars: conditional Granger causality and Lasso regression. Departing from a recent information theory proof that Granger causality and transfer entropy are equivalent, we propose a heuristic method for the identification of true structural dependencies in multivariate economic time series. Through rigorous testing, both empirically and through simulations, the present paper proves that genetic algorithms initialized with classical solutions are able to easily break the symmetry of random search and progress towards specific modeling.

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Brovelli, Andrea. "Statistical Analysis of Single-Trial Granger Causality Spectra." Computational and Mathematical Methods in Medicine 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/697610.

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Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based ont-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

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Seth, S., and J. C. Principe. "Assessing Granger Non-Causality Using Nonparametric Measure of Conditional Independence." IEEE Transactions on Neural Networks and Learning Systems 23, no. 1 (January 2012): 47–59. http://dx.doi.org/10.1109/tnnls.2011.2178327.

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KATIRCIOǦLU, SALIH TURAN. "TOURISM AND GROWTH IN SINGAPORE: NEW EXTENSION FROM BOUNDS TEST TO LEVEL RELATIONSHIPS AND CONDITIONAL GRANGER CAUSALITY TESTS." Singapore Economic Review 56, no. 03 (August 2011): 441–53. http://dx.doi.org/10.1142/s0217590811004365.

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This paper empirically investigates the tourism-led growth (TLG) hypothesis in the case of Singapore by employing the bounds test to cointegration, error correction models and Granger causality tests using annual data from 1960 to 2007. Results confirm the existence of long-term equilibrium relationship between international tourism and economic growth in the case of Singapore; real income growth converges to its long-term equilibrium level significantly by 51.4% in the TLG model. The major finding of this study is that the TLG hypothesis is confirmed for the Singaporean economy in the long-term as a result of conditional Granger causality tests.

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Ai, Dongmei, Xiaoxin Li, Gang Liu, Xiaoyi Liang, and Li Xia. "Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality." Genes 10, no. 3 (March 14, 2019): 216. http://dx.doi.org/10.3390/genes10030216.

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The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

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Nakajima, Tadahiro. "Test for volatility spillover effects in Japan’s oil futures markets by a realized variance approach." Studies in Economics and Finance 36, no. 2 (June 24, 2019): 224–39. http://dx.doi.org/10.1108/sef-01-2017-0011.

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Purpose The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. Design/methodology/approach The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these variables using the vector autoregression model. Further, the author estimates the EGARCH model based on daily data and test for Granger causality in variance between commodity futures using Hong’s (2001) approach. Findings The results of the RV approach reveal that the hypothesis on the existence of a mutual volatility spillover between crude oil and petroleum product markets is accepted. However, the results of the conventional approach indicate that all the hypotheses on Granger causalities in variance are rejected. The methodology based on intraday high-frequency data exhibits higher power than the conventional approach based on daily data. Originality/value This is the first paper to investigate Japan’s oil market using RV. The authors conclude that the approach based on RV is universally adoptable when testing for Granger causality in variance.

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Mainali, Kumar, Sharon Bewick, Briana Vecchio-Pagan, David Karig, and William F. Fagan. "Detecting interaction networks in the human microbiome with conditional Granger causality." PLOS Computational Biology 15, no. 5 (May 20, 2019): e1007037. http://dx.doi.org/10.1371/journal.pcbi.1007037.

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Taamouti, Abderrahim, Taoufik Bouezmarni, and Anouar El Ghouch. "Nonparametric estimation and inference for conditional density based Granger causality measures." Journal of Econometrics 180, no. 2 (June 2014): 251–64. http://dx.doi.org/10.1016/j.jeconom.2014.03.001.

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Dhifaoui, Zouhaier, and Faicel Gasmi. "Linear and nonlinear linkage of conditional stochastic volatility of interbank interest rates: Empirical evidence of the BRICS countries." BRICS Journal of Economics 2, no. 2 (July 30, 2021): 4–16. http://dx.doi.org/10.38050/2712-7508-2021-2-1.

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The purpose of this article is to detect a possible linear and nonlinear causal relationship between the conditional stochastic volatility of log return of interbank interest rates for the BRICS countries in the period from January 2015 to October 2018. To extract the volatility of the analyzed time series, we use a stochastic volatility model with moving average innovations. To test a causal relationship between the estimated stochastic volatilities, two steps are applied. First, we used the Granger causality test and a vector autoregressive model (VAR). Secondly, we applied the nonlinear Granger causality test to the raw data to explore a new nonlinear causal relationship between stochastic volatility time series, and also applied it to the residual of the VAR model to confirm the causality detected in the first step. This study demonstrates the existence of some unidirectional/bidirectional linear/nonlinear causal relationships between the studied stochastic volatility time series.

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Ballester, Laura, Ana Mónica Escrivá, and Ana González-Urteaga. "The Nexus between Sovereign CDS and Stock Market Volatility: New Evidence." Mathematics 9, no. 11 (May 25, 2021): 1201. http://dx.doi.org/10.3390/math9111201.

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This paper extends the studies published to date by performing an analysis of the causal relationships between sovereign CDS spreads and the estimated conditional volatility of stock indices. This estimation is performed using a vector autoregressive model (VAR) and dynamically applying the Granger causality test. The conditional volatility of the stock market has been obtained through various univariate GARCH models. This methodology allows us to study the information transmissions, both unidirectional and bidirectional, that occur between CDS spreads and stock volatility between 2004 and 2020. We conclude that CDS spread returns cause (in the Granger sense) conditional stock volatility, mainly in Europe and during the sovereign debt crisis. This transmission dynamic breaks down during the COVID-19 period, where there are high bidirectional relationships between the two markets.

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Xiaoguang Liu, Xiaoguang Liu, Renping Song Xiaoguang Liu, Guoliang Ding Renping Song, Mingxia Zu Guoliang Ding, Xiaomei Wang Mingxia Zu, and Yuan Wang Xiaomei Wang. "A Prediction Model for Substation Investment Benefit Based on Granger Causality." 電腦學刊 33, no. 6 (December 2022): 107–17. http://dx.doi.org/10.53106/199115992022123306009.

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<p>The construction of a lean operation and inspection integrated management system for substations is an important part of the development and maintenance of the power system. Forecasting the investment benefits of substation project development is an important issue in feasibility analysis. Therefore, we need to use a highly accurate method to make a prediction of the investment benefit of this project. Granger causation is a causal relationship based on "prediction", and inferring about its causality is a key task in time series analysis. In this paper, we propose a new estimation method, Granger causality estimation based on supervised learning. This method uses an eigenvalue representation of the distance between conditional distributions conditioned on past values. And for different time series, the method can give different feature vectors. Applying it to the prediction of the investment efficiency of the substation can achieve a good prediction effect. Therefore, we used granger causality to build a predictive model of the return on investment in substations.</p> <p> </p>

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Falasca, N. W., and R. Franciotti. "Ability of Granger Causality Analysis to Detect Indirect Links: A Simulation Study." Nonlinear Phenomena in Complex Systems 23, no. 2 (July 9, 2020): 121–24. http://dx.doi.org/10.33581/1561-4085-2020-23-2-121-124.

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Granger causality (G-causality) has emerged as a useful tool to investigate the influence that one system can exert over another system, but challenges remain when applying it to biological data. Specifically, it is not clear if G-causality can distinguish between direct and indirect influences. In this study time domain G-causality connectivity analysis was performed on simulated electroencephalographic cerebral signals. Conditional multivariate autoregressive model was applied to 19 virtual time series (nodes) to identify the effects of direct and indirect links while varying one of the following variables: the length of the time series, the lags between interacting nodes, the connection strength of the links, and the noise. Simulated data revealed that weak indirect influences are not identified by Gcausality analysis when applied on covariance stationary, non-correlated electrophysiological time series.

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Xi, Zhuo. "The Asymmetric Volatility Spillover and Dynamic Correlation Across Equity Markets in China and The United States." International Journal of Business & Management Studies 04, no. 06 (June 12, 2023): 31–43. http://dx.doi.org/10.56734/ijbms.v4n6a5.

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This paper aims to study the volatility spillover effects as well as the dynamic conditional correlation between stock market returns in China and the U.S. Firstly, the analysis uses a vector autoregression with a bivariate BEKK-GARCH model to capture the asymmetric volatility transmissions between the two markets during the sample of 1996-2019. Then a VAR-DCC-GARCH model is employed to estimate the dynamic conditional correlation between these two market returns. Finally, linear regression and Granger Causality test are conducted to further explore the effect of the U.S policy rates on such correlation. In order to account for the U.S monetary stances during the unconventional period, a combination of Fed fund rates and Shadow rates developed by Wu and Xia (2016) is used as policy rates. The main empirical results suggest (1) evidence of unidirectional volatility spillover from the U.S. to China market; but no spillover from China to U.S; (2) the dynamics of the conditional correlations from the VAR-DCC-GARCH model exhibit increases in correlation between the stock returns of China and U.S after 2008 financial crisis and recent trade war; (3) a linear regression shows that there is negative relationship between U.S policy rates and the dynamic conditional correlation, with the correlation coefficient r=-0.62. Granger Causality test suggests that the U.S policy rates do cause the change of the conditional correlation but not the other way around.

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Maneejuk, Paravee, and Woraphon Yamaka. "The Role of Economic Contagion in the Inward Investment of Emerging Economies: The Dynamic Conditional Copula Approach." Mathematics 9, no. 20 (October 10, 2021): 2540. http://dx.doi.org/10.3390/math9202540.

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Contagion has been one of the most widely studied and challenging problems in recent economic research. This paper aims at capturing the main impact of contagion risk of the U.S. on foreign direct investment inflows in 18 emerging countries. To quantify the degree of contagion, the time-varying tail dependence copula is employed. Then, the Granger causality test and time series regression analysis are used to investigate the temporal and contemporaneous effects of contagion risk on investment inflows, respectively. Overall, the results confirm the time-varying contagion effects of the U.S. economy on 18 emerging economies. The size of contagion effects gradually increases for all countries, except Thailand, the Philippines, Argentina, and Chile. Furthermore, the results of the Granger causality test and regression reveal that temporal and contemporaneous effects of contagion risk on investment inflows exist in 8 out of 18 countries.

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Bouezmarni, Taoufik, Jeroen V. K. Rombouts, and Abderrahim Taamouti. "Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality." Journal of Business & Economic Statistics 30, no. 2 (April 2012): 275–87. http://dx.doi.org/10.1080/07350015.2011.638831.

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Liao, Wei, Dante Mantini, Zhiqiang Zhang, Zhengyong Pan, Jurong Ding, Qiyong Gong, Yihong Yang, and Huafu Chen. "Evaluating the effective connectivity of resting state networks using conditional Granger causality." Biological Cybernetics 102, no. 1 (November 25, 2009): 57–69. http://dx.doi.org/10.1007/s00422-009-0350-5.

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Lawal, Gold Olamide, Bisola Aladenika, Seyi Saint Akadiri, Ayodeji Samson Fatigun, and Victoria Olushola Olanrewaju. "Geopolitical Risk, Globalization and Environmental Degradation in South Africa: Evidence from Advanced Quantiles Approach." Problemy Ekorozwoju 18, no. 1 (January 1, 2023): 207–15. http://dx.doi.org/10.35784/pe.2023.1.22.

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Sustainable development involves the incorporation of socio-economic concerns and environmental protection into the economic decision-making process, in such a way that, any developmental effort would eventually be favorable to immediate and future generations. It is against this backdrop this study investigates the effects of geopolitical risk and globalization on environmental degradation in South Africa over the period 1985Q1-2018Q4. This study improves on existing studies and raises concerns on the potential twin-effect of geopolitical risk and globalization on the environment. We deviate from the existing studies that make use of the mean causality approaches that do not consider possible dependence in the conditional tail of the series distribution. To examine whether the causality exists among the series, we make use of the novel Troster (2018) Granger non-causality in condition quantiles, which captures the pattern of causality in various quantiles. Empirical results show that there is feedback causality nexus between geopolitical risk and CO2 emissions. In majority of the quantiles, feedback causality is also observed between globalization and CO2 emissions. We find a bidirectional Granger causality nexus between geopolitical risk and environmental degradation, and between globalization and environmental degradation. Globalization and geopolitical risk negatively influence environmental degradation. We conclude that environmental degradation is not driven by globalization and geopolitical risk in South Africa, among other policy suggestions.

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Корнилов, М. В., and И. В. Сысоев. "Реконструкция архитектуры связей в цепочке из трех однонаправленно связанных систем методом причинности по Грейнджеру." Письма в журнал технической физики 44, no. 10 (2018): 86. http://dx.doi.org/10.21883/pjtf.2018.10.46103.17201.

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AbstractWe propose a method for reconstructing links in a chain of unidirectionally coupled systems by means of three tests for estimating direct and mediated coupling with the aid of Granger conditional causality in terms of prognostic models with polynomial nonlinearity. It is shown that this approach allows the architecture of links in these systems to be correctly identified in more than 80% of cases.

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Seghouane, Abd-Krim, and Shun-ichi Amari. "Identification of Directed Influence: Granger Causality, Kullback-Leibler Divergence, and Complexity." Neural Computation 24, no. 7 (July 2012): 1722–39. http://dx.doi.org/10.1162/neco_a_00291.

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Detecting and characterizing causal interdependencies and couplings between different activated brain areas from functional neuroimage time series measurements of their activity constitutes a significant step toward understanding the process of brain functions. In this letter, we make the simple point that all current statistics used to make inferences about directed influences in functional neuroimage time series are variants of the same underlying quantity. This includes directed transfer entropy, transinformation, Kullback-Leibler formulations, conditional mutual information, and Granger causality. Crucially, in the case of autoregressive modeling, the underlying quantity is the likelihood ratio that compares models with and without directed influences from the past when modeling the influence of one time series on another. This framework is also used to derive the relation between these measures of directed influence and the complexity or the order of directed influence. These results provide a framework for unifying the Kullback-Leibler divergence, Granger causality, and the complexity of directed influence.

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Chowdhury, Abdur. "Inflation and inflation-uncertainty in India: the policy implications of the relationship." Journal of Economic Studies 41, no. 1 (January 7, 2014): 71–86. http://dx.doi.org/10.1108/jes-04-2012-0046.

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Purpose – Inflation and its related uncertainty can impose costs on real economic output in any economy. This paper aims to analyze the relationship between inflation and inflation uncertainty in India. Design/methodology/approach – The methodology uses a generalized autoregressive conditional heteroscedasticity (GARCH) model and Granger Causality test. Findings – Initial estimates show the inflation rate to be a stationary process. The maximum likelihood estimates from the GARCH model reveal strong support for the presence of a positive relationship between the level of inflation and its uncertainty. The Granger causality results indicate a feedback between inflation and uncertainty. Research limitations/implications – The research results have important implication for policy makers and especially the Reserve Bank of India. Practical implications – It provides strong support to the notion of an opportunistic central bank in India. Originality/value – The results of the paper are of relevance not only to the monetary policy makers but also to academicians in India and other developing countries.

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Hasbullah, Endang Soeryana, Endang Rusyaman, and Alit Kartiwa. "THE GARCH MODEL VOLATILITY OF SHARIA STOCKS ASSOCIATED CAUSALITY WITH MARKET INDEX." International Journal of Quantitative Research and Modeling 1, no. 1 (February 2, 2020): 18–28. http://dx.doi.org/10.46336/ijqrm.v1i1.3.

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The purpose of this paper is to examine the volatility of Islamic stocks related to the causality of the composite stock price index (CSPI). The aim is to investigate the causality of several levels of stock returns with the movement of the CSPI, and determine its volatility as a measure of risk. To determine the causality relationship is done by using the granger causality test method, with Vector Autoregressive (VAR) modeling. Whereas to determine the volatility is done using the Generalized Autoregressive Conditional Heteroscedastisiy (GARCH) model approach. The results of the causality test show that there is a direct relationship that affects and is influenced by the CSPI, and the relationship that affects each other between the company's stock market and the movement of the CSPI. While the volatility follows the GARCH model (1, 1). Based on the results of this study are expected to be used as consideration in making investment decisions in the analyzed stocks.

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Nath Sahu, Tarak, Kalpataru Bandopadhyay, and Debasish Mondal. "An empirical study on the dynamic relationship between oil prices and Indian stock market." Managerial Finance 40, no. 2 (January 7, 2014): 200–215. http://dx.doi.org/10.1108/mf-06-2013-0131.

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Purpose – This study aims to investigate the dynamic relationships between oil price shocks and Indian stock market. Design/methodology/approach – The study used daily data for the period starting from January 2001 to March 2013. In this study, Johansen's cointegration test, vector error correction model (VECM), Granger causality test, impulse response functions (IRFs) and variance decompositions (VDCs) test have been applied to exhibit the long-run and short-run relationship between them. Findings – The cointegration result indicates the existence of long-term relationship. Further, the error correction term of VECM shows a long-run causality moves from Indian stock market to oil price but not the vice versa. The results of the Granger causality test under the VECM framework confirm that no short-run causality between the variables exists. The VDCs analysis revealed that the Indian stock markets and crude oil prices are strongly exogenous. Finally, from the IRFs, analysis revealed that a positive shock in oil price has a small but persistence and growing positive impact on Indian stock markets in short run. Originality/value – The study would enhance the understandings of the interaction between oil price volatilities and emerging stock market performances. Further, the study would enable foreign investors who are interested in Indian stock market helps in understanding the conditional relationship between the variables.

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Yurdakul, Funda, and Erdogan Cevher. "Determinants of Current Account Deficit in Turkey: The Conditional and Partial Granger Causality Approach." Procedia Economics and Finance 26 (2015): 92–100. http://dx.doi.org/10.1016/s2212-5671(15)00884-9.

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Chen, Yonghong, Steven L. Bressler, and Mingzhou Ding. "Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data." Journal of Neuroscience Methods 150, no. 2 (January 2006): 228–37. http://dx.doi.org/10.1016/j.jneumeth.2005.06.011.

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Zhou, Zhenyu, Xunheng Wang, Nelson J. Klahr, Wei Liu, Diana Arias, Hongzhi Liu, Karen M. von Deneen, et al. "A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging." Magnetic Resonance Imaging 29, no. 3 (April 2011): 418–33. http://dx.doi.org/10.1016/j.mri.2010.10.008.

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Yamaka, Woraphon, Jianxu Liu, Mingyang Li, Paravee Maneejuk, and Hai Q. Dinh. "Analyzing the Causality and Dependence between Exchange Rate and Real Estate Prices in Boom-and-Bust Markets: Quantile Causality and DCC Copula GARCH Approaches." Axioms 11, no. 3 (March 3, 2022): 113. http://dx.doi.org/10.3390/axioms11030113.

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Unlike most previous studies examining the causal relationship and dependence between exchange rates and real estate prices, this study aims to investigate the causal relationship and dependence between these two variables in a boom-and-bust market setting using the panel quantile Granger causality and dynamic conditional correlation (DCC) copula GARCH approaches, respectively. In the panel quantile Granger causality test, quantiles 0.1 and 0.9 are considered to represent extreme markets (bust and boom, respectively). Our first results showed the causal effects at extreme quantiles to be very different from those at the median quantile. We also found a greater causality between house prices and exchange rates in the boom market compared to the bust market. In the second model, we explored the relationship between exchange rates and real estate prices, taking boom-and-bust dynamics into account by measuring the tail dependence through the DCC copula GARCH method. Our findings confirm the strong time-varying tail dependence between house prices and exchange rates. The degree of tail dependence was quite stable over the sample period, except for the periods around 1997–1998 and 2008–2009, when the degree of tail dependence was stronger and less persistent. These two periods correspond to the two great financial crises in Asia and the USA, respectively.

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Oliveira, Wendy Sidon Meira de, and André Nunes Maranhão. "Spillovers de Volatilidades Cambiais e de Mercados Financeiros Internacionais no Mercado Acionário Brasileiro." Brazilian Review of Finance 15, no. 4 (June 20, 2018): 569. http://dx.doi.org/10.12660/rbfin.v15n4.2017.63341.

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We present in this study the results of volatility spillover in the Brazilian stock market, measured by conditional correlations. Using GARCH multivariate conditional correlations were estimated at 3 different models combining the Ibovespa index of the three types of exchange rate shocks and a shock of international financial markets. The existence and direction of spillovers of volatility of forward exchange shocks, international financial market shocks and the Ibovespa were tested by Granger causality test of second order. The results show the existence of spillovers from exchange rate shocks and financial markets for the Ibovespa index, and these correlations have temporal dynamics, with spillovers always in the direction of the shocks to the Ibovespa index.

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Detto, Matteo, Gil Bohrer, Jennifer Nietz, Kyle Maurer, Chris Vogel, Chris Gough, and Peter Curtis. "Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest." Entropy 15, no. 12 (October 9, 2013): 4266–84. http://dx.doi.org/10.3390/e15104266.

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Chen, Han-Sheng, Zhengbing Yan, Xuelei Zhang, Yi Liu, and Yuan Yao. "Root Cause Diagnosis of Process Faults Using Conditional Granger Causality Analysis and Maximum Spanning Tree." IFAC-PapersOnLine 51, no. 18 (2018): 381–86. http://dx.doi.org/10.1016/j.ifacol.2018.09.330.

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Ogede, Jimoh, Musa Oduola, Olumuyiwa Yinusa, and Lukman Raimi. "Modelling the influence of financial inclusion on the remittance growth nexus in Nigeria." Ekonomski anali 68, no. 237 (2023): 137–63. http://dx.doi.org/10.2298/eka2337137s.

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In this paper, we explore the nexus between remittances and Nigeria?s economic growth over the period 1996 to 2020 from the perspective of financial inclusion (FI). The fully modified ordinary least square (FMOLS) and Granger (1969) causality methodologies were employed. The findings of the FMOLS show that the increasing flow of remittances can significantly contribute to the growth of the Nigerian economy. Also, the interaction of financial inclusion and remittances has a significant impact on the country?s development. The study concludes that the interaction of remittances with the measures of financial inclusion will lead to economic growth at a faster rate than when there is no interaction with financial inclusion. Using the Granger causality test, the study revealed that the relationship between financial inclusion and economic growth is a unidirectional one. It shows that the impact of financial inclusion on growth is conditional on remittances. Therefore, Nigeria?s authorities need to work to strengthen all existing institutional weaknesses that allow questionable transactions in financial markets and to promote a more inclusive financial sector that will reduce the number of unbanked individuals in the country.

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Tiwari, Aviral Kumar, and Phouphet Kyophilavong. "Exchange Rates and International Reserves in India." South Asia Economic Journal 18, no. 1 (March 2017): 76–93. http://dx.doi.org/10.1177/1391561416684237.

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This article aims to study the relationship between real effective exchange rate (REER) and international reserve in India by applying the bivariate and conditional bivariate Granger causality test in frequency domain framework proposed by Breitung and Candelon (2006). The variables that are included to condition the frequency domain are the industrial production index, stock prices and wholesale producer index. Results found the evidence of business cyclical causality running from international reserve to REER for frequencies between 0.01 and 1.63 that corresponds to the 4 months and higher months cycles in India. The results have a strong bearing on the policy implications of India and any country alike it. The study concludes that the Reserve Bank of India should consider exchange rate as a grave determinant to manage appropriate forex reserve.

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Yadav, Miklesh Prasad, and Asheesh Pandey. "Volatility Spillover Between Indian and MINT Stock Exchanges: Portfolio Diversification Implication." Indian Economic Journal 67, no. 3-4 (December 2019): 299–311. http://dx.doi.org/10.1177/0019466220947501.

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We examine the spillover effect from the Indian stock market to Mexico, Indonesia, Nigeria and Turkey (MINT) stock markets in order to check if suitable diversification opportunities are available to global portfolio managers investing in India. We apply Granger causality test, vector auto-regression (VAR) and dynamic conditional correlation (DCC)–MGARCH to investigate the level of integration between India and MINT economies. We observe bidirectional causality between India and Nigeria, unidirectional causality in Mexico and Indonesia, while no causality is found between India and Turkey. Our VAR results suggest that none of the MINT economies impact the return of the Indian stock market; rather returns of the Indian stock market are more affected by their own lagged values. Finally, by applying DCC–MGARCH, we observe that there is no volatility spillover from India to any of the MINT economies. We recommend that portfolio managers investing in the Indian economy may explore MINT economies as possible destinations to diversify their risk. Our study has implications for both academia and portfolio managers.

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Singh, Amanjot, and Manjit Singh. "Conditional co-movement and dynamic interactions: US and BRIC equity markets." Ekonomski anali 62, no. 212 (2017): 85–111. http://dx.doi.org/10.2298/eka1712085s.

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The present study attempts to capture conditional or time-varying co-movement and dynamic interactions between the US and BRIC (Brazil, Russia, India, and China) equity markets across the sample period 2004 to 2014 by employing diverse econometric models. The sample period is further divided into three different sub-periods concerning the US financial crisis period, viz. pre-crisis, crisis, and post-crisis periods. The vector autoregression- dynamic conditional correlation-multivariate asymmetric generalized autoregressive conditional heteroskedastic [VAR-DCC-MVAGARCH (1.1)] model and Toda-Yamamoto?s (1995) Granger causality tests are employed for the purpose of overall analysis in a multivariate framework. The results report the existence of time-varying co-movement between the US and BRIC equity markets, whereby co-movement between the US and Brazilian markets is found to be the highest, followed by the Russian, Indian, and Chinese equity markets. Dynamic interactions are also registered between the respective US/BRIC comovements during different sub-periods. The results have important implications for market participants and policymakers.

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Gao, Qing, Xujun Duan, and Huafu Chen. "Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality." NeuroImage 54, no. 2 (January 2011): 1280–88. http://dx.doi.org/10.1016/j.neuroimage.2010.08.071.

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Casile, Antonino, Rose T. Faghih, and Emery N. Brown. "Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains." PLOS Computational Biology 17, no. 1 (January 25, 2021): e1007675. http://dx.doi.org/10.1371/journal.pcbi.1007675.

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Assessing directional influences between neurons is instrumental to understand how brain circuits process information. To this end, Granger causality, a technique originally developed for time-continuous signals, has been extended to discrete spike trains. A fundamental assumption of this technique is that the temporal evolution of neuronal responses must be due only to endogenous interactions between recorded units, including self-interactions. This assumption is however rarely met in neurophysiological studies, where the response of each neuron is modulated by other exogenous causes such as, for example, other unobserved units or slow adaptation processes. Here, we propose a novel point-process Granger causality technique that is robust with respect to the two most common exogenous modulations observed in real neuronal responses: within-trial temporal variations in spiking rate and between-trial variability in their magnitudes. This novel method works by explicitly including both types of modulations into the generalized linear model of the neuronal conditional intensity function (CIF). We then assess the causal influence of neuron i onto neuron j by measuring the relative reduction of neuron j’s point process likelihood obtained considering or removing neuron i. CIF’s hyper-parameters are set on a per-neuron basis by minimizing Akaike’s information criterion. In synthetic data sets, generated by means of random processes or networks of integrate-and-fire units, the proposed method recovered with high accuracy, sensitivity and robustness the underlying ground-truth connectivity pattern. Application of presently available point-process Granger causality techniques produced instead a significant number of false positive connections. In real spiking responses recorded from neurons in the monkey pre-motor cortex (area F5), our method revealed many causal relationships between neurons as well as the temporal structure of their interactions. Given its robustness our method can be effectively applied to real neuronal data. Furthermore, its explicit estimate of the effects of unobserved causes on the recorded neuronal firing patterns can help decomposing their temporal variations into endogenous and exogenous components.

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Karagianni, Stella, Maria Pempetzoglou, and Anastasios Saraidaris. "Government Expenditures and Economic Growth: a Nonlinear Causality Investigation for the UK." European Journal of Marketing and Economics 2, no. 2 (May 31, 2019): 52. http://dx.doi.org/10.26417/ejme-2019.v2i2-70.

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This study aims to explore the causal relationship between government expenditures and economic growth in the UK. The analysis emphasizes on the nonlinearity facet of the explored causality. In this aspect, existing conditional heteroscedasticity as a potential source of bias, is filtered out with the use of the nonparametric Diks and Panchenko causality test. The UK government expenditures are disaggregated into total managed expenditure (TGE), current expenditure (CGE) and net investment (IGE), in order to account for a possible heterogeneity in a causality disclosure linked to the nature of expenditures. The findings support that UK government spending Granger causes nonlinearly UK economic growth. Overall, government spending at all three levels of disaggregation is documented to influence the economic growth in the UK. In this aspect, the results move along with the endogenous growth literature. However, in a policy making framework, the disclosed nonlinearity patterns stress the high risk involved whenever economic growth is pursued restrictively via public spending policies overlooking other important elements of the economic life (e.g. market structure, macroeconomic environment, etc.). Additionally, the exhibited nonlinearity in the examined causality could be regarded as a likely cause of the widespread diversification of the findings in the field empirical literature.

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Singh, Amanjot, and Manjit Singh. "A revisit to how linkages fuel dependent economic policy initiatives." International Journal of Law and Management 59, no. 6 (November 13, 2017): 1068–108. http://dx.doi.org/10.1108/ijlma-08-2016-0074.

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Purpose The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a). Design/methodology/approach Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used. Findings Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system. Originality/value The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

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

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