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Muhammad Shahbaz - One of the best experts on this subject based on the ideXlab platform.
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does renewable energy consumption add in economic growth an application of auto regressive distributed lag model in pakistan
Renewable & Sustainable Energy Reviews, 2015Co-Authors: Muhammad Shahbaz, Nanthakumar Loganathan, Mohammad Zeshan, Khalid ZamanAbstract:Abstract The objective of the study is to examine the relationship between renewable energy consumption and economic growth by incorporating capital and labour as potential determinants of production function in case of Pakistan. This study used auto-regressive distributed lag (ARDL) model and rolling window approach (RWA) for cointegration in context of Pakistan. The study used quarterly data over the period of 1972Q1–2011Q4. The Causality Analysis applied through VECM Granger Causality and innovative accounting approaches. The results reveal that all the variables in the study are cointegrated that shows the long run relationship between the variables. Furthermore, renewable energy consumption, capital and labour boost economic growth. The Causality Analysis shows the feedback effect between economic growth and renewable energy consumption.
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financial development and poverty reduction nexus a cointegration and Causality Analysis in bangladesh
Research Papers in Economics, 2014Co-Authors: Gazi Salah Uddin, Muhammad Shahbaz, Mohamed El Hedi Arouri, Frederic TeulonAbstract:this study investigates the relationship between financial development, economic growth and poverty reduction in Bangladesh using quarter frequency data over the period of 1975-2011
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financial development and poverty reduction nexus a cointegration and Causality Analysis in bangladesh
Economic Modelling, 2014Co-Authors: Gazi Salah Uddin, Muhammad Shahbaz, Mohamed El Hedi Arouri, Frederic TeulonAbstract:This study investigates the relationship between financial development, economic growth and poverty reduction in Bangladesh using quarter frequency data over the period of 1975-2011. This issue is of importance for developing economics, since the role of financial sector in mobilizing and allocating savings into productive investments. All variables are tested for their order of integration using the ADF and Zivot-Andrews structural break tests. The results show that the variables are integrated at I(1). We then apply a simulation based the ARDL approach to cointegration by incorporating structural breaks stemming in the series for long run relation. Our empirical findings indicated that long run relationship between financial development, economic growth and poverty reduction exists in Bangladesh. The diagnostic tests show that the underlying assumptions of the statistical model are fulfilled. The implication of the empirical findings is explained in the main text.
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do imports and foreign capital inflows lead economic growth cointegration and Causality Analysis in pakistan
South Asia Economic Journal, 2013Co-Authors: Mohammad Mafizur Rahman, Muhammad ShahbazAbstract:The article investigates the impacts of imports and foreign capital inflows on economic growth of Pakistan over the period 1990 to 2010. We have applied the structural break autoregressive distributed lag (ARDL) bounds testing approach to cointegration to examine the long-run relationship between the variables. The vector error correction model (VECM) Granger Causality multivariate framework is used to investigate the causal relationship between the series. Empirical Analysis in this article confirms the long-run relationship between foreign capital inflows, imports and economic growth. The results indicate that foreign capital inflows and imports have positive and significant effect on economic growth in case of Pakistan. Causality Analysis reveals the bidirectional Causality between the variables, but strong causal relation is running from foreign capital inflows and imports to economic growth.
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the environmental kuznets curve and the role of coal consumption in india cointegration and Causality Analysis in an open economy
Renewable & Sustainable Energy Reviews, 2013Co-Authors: Aviral Kumar Tiwari, Muhammad Shahbaz, Qazi Muhammad Adnan HyeAbstract:Abstract This study investigates the dynamic relationship between coal consumption, economic growth, trade openness and CO2 emissions in case of India. In doing so, Narayan and Popp, Journal of Applied Statistics 2010; 37:1425–1438, structural break unit test is applied to test the order of integration of the variables. Long run relationship between the variables is tested by applying the ARDL bounds testing approach to cointegration developed by Pesaran et al. Journal of Applied Econometrics 2001; 16:289–326. The results confirm the existence of cointegration for long run between coal consumption, economic growth, trade openness and CO2 emissions. Our empirical exercise indicates the presence of environmental Kuznets curve (EKC) in long run as well as in short run. Coal consumption as well as trade openness contributes to CO2 emissions. The Causality Analysis reports the feedback hypothesis between economic growth and CO2 emissions and same inference is drawn between coal consumption and CO2 emissions. Moreover, trade openness Granger causes economic growth, coal consumption and CO2 emissions.
Saban Nazlioglu - One of the best experts on this subject based on the ideXlab platform.
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oil price agricultural commodity prices and the dollar a panel cointegration and Causality Analysis
Energy Economics, 2012Co-Authors: Saban Nazlioglu, Ugur SoytasAbstract:This study examines the dynamic relationship between world oil prices and twenty four world agricultural commodity prices accounting for changes in the relative strength of US dollar in a panel setting. We employ panel cointegration and Granger Causality methods for a panel of twenty four agricultural products based on monthly prices ranging from January 1980 to February 2010. The empirical results provide strong evidence on the impact of world oil price changes on agricultural commodity prices. Contrary to the findings of many studies in the literature that report neutrality of agricultural prices to oil price changes, we find strong support for the role of world oil prices on prices of several agricultural commodities. The positive impact of a weak dollar on agricultural prices is also confirmed.
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world oil and agricultural commodity prices evidence from nonlinear Causality
Energy Policy, 2011Co-Authors: Saban NazliogluAbstract:Abstract The increasing co-movements between the world oil and agricultural commodity prices have renewed interest in determining price transmission from oil prices to those of agricultural commodities. This study extends the literature on the oil–agricultural commodity prices nexus, which particularly concentrates on nonlinear causal relationships between the world oil and three key agricultural commodity prices (corn, soybeans, and wheat). To this end, the linear Causality approach of Toda–Yamamoto and the nonparametric Causality method of Diks–Panchenko are applied to the weekly data spanning from 1994 to 2010. The linear Causality Analysis indicates that the oil prices and the agricultural commodity prices do not influence each other, which supports evidence on the neutrality hypothesis. In contrast, the nonlinear Causality Analysis shows that: (i) there are nonlinear feedbacks between the oil and the agricultural prices, and (ii) there is a persistent unidirectional nonlinear Causality running from the oil prices to the corn and to the soybeans prices. The findings from the nonlinear Causality Analysis therefore provide clues for better understanding the recent dynamics of the agricultural commodity prices and some policy implications for policy makers, farmers, and global investors. This study also suggests the directions for future studies.
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financial development and economic growth nexus in the mena countries bootstrap panel granger Causality Analysis
Economic Modelling, 2011Co-Authors: Saban Nazlioglu, Huseyin AgirAbstract:This paper investigates the direction of Causality between financial development and economic growth in the Middle East and North African (MENA) countries. The panel Causality testing approach, developed by Konya (2006) [Konya, L. (2006), exports and growth: Granger Causality Analysis on OECD countries with a panel data approach, Economic Modelling, 23, 978–992], based on the Seemingly Unrelated Regressions and Wald tests with the country specific bootstrap critical values, is applied to the panel of fifteen MENA countries for the period 1980–2007. In order to capture the different aspects of financial development, six different indicators are used. Empirical results show that there is no clear consensus on the direction of Causality between financial development and economic growth for all measurements of financial development and it is also observed that the findings are country specific.
Gopikrishna Deshpande - One of the best experts on this subject based on the ideXlab platform.
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effect of hemodynamic variability on granger Causality Analysis of fmri
NeuroImage, 2010Co-Authors: Gopikrishna Deshpande, Krishnankutty SathianAbstract:In this work, we investigated the effect of the regional variability of the hemodynamic response on the sensitivity of Granger Causality (GC) Analysis of functional magnetic resonance imaging (fMRI) data to neuronal causal influences. We simulated fMRI data by convolving a standard canonical hemodynamic response function (HRF) with local field potentials (LFPs) acquired from the macaque cortex and manipulated the causal influence and neuronal delays between the LFPs, the hemodynamic delays between the HRFs, the signal-to-noise ratio (SNR), and the sampling period (TR) to assess the effect of each of these factors on the detectability of the neuronal delays from GC Analysis of fMRI. In our first bivariate implementation, we assumed the worst-case scenario of the hemodynamic delay being at the empirical upper limit of its normal physiological range and opposing the direction of neuronal delay. We found that, in the absence of HRF confounds, even tens of milliseconds of neuronal delays can be inferred from fMRI. However, in the presence of HRF delays which opposed neuronal delays, the minimum detectable neuronal delay was hundreds of milliseconds. In our second multivariate simulation, we mimicked the real situation more closely by using a multivariate network of four time series and assumed the hemodynamic and neuronal delays to be unknown and drawn from a uniform random distribution. The resulting accuracy of detecting the correct multivariate network from fMRI was well above chance and was up to 90% with faster sampling. Generically, under all conditions, faster sampling and low measurement noise improved the sensitivity of GC Analysis of fMRI data to neuronal Causality.
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assessing and compensating for zero lag correlation effects in time lagged granger Causality Analysis of fmri
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Gopikrishna Deshpande, Krishnankutty SathianAbstract:Effective connectivity in brain networks can be studied using Granger Causality Analysis, which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger Causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this paper showed that the zero-lag correlation does “leak” into estimates of time-lagged Causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger Causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.
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multivariate granger Causality Analysis of fmri data
Human Brain Mapping, 2009Co-Authors: Gopikrishna Deshpande, Stephan Laconte, George Andrew James, Scott PeltierAbstract:This article describes the combination of multivariate Granger Causality Analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) Analysis of multivariate Granger Causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc.
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effective connectivity during haptic perception a study using granger Causality Analysis of functional magnetic resonance imaging data
NeuroImage, 2008Co-Authors: Gopikrishna Deshpande, Randall Stilla, Krishnankutty SathianAbstract:Although it is accepted that visual cortical areas are recruited during touch, it remains uncertain whether this depends on top-down inputs mediating visual imagery or engagement of modality-independent representations by bottom-up somatosensory inputs. Here we addressed this by examining effective connectivity in humans during haptic perception of shape and texture with the right hand. Multivariate Granger Causality Analysis of functional magnetic resonance imaging (fMRI) data was conducted on a network of regions that were shape- or texture-selective. A novel network reduction procedure was employed to eliminate connections that did not contribute significantly to overall connectivity. Effective connectivity during haptic perception was found to involve a variety of interactions between areas generally regarded as somatosensory, multisensory, visual and motor, emphasizing flexible cooperation between different brain regions rather than rigid functional separation. The left postcentral sulcus (PCS), left precentral gyrus and right posterior insula were important sources of connections in the network. Bottom-up somatosensory inputs from the left PCS and right posterior insula fed into visual cortical areas, both the shape-selective right lateral occipital complex (LOC) and the texture-selective right medial occipital cortex (probable V2). In addition, top-down inputs from left postero-supero-medial parietal cortex influenced the right LOC. Thus, there is strong evidence for the bottom-up somatosensory inputs predicted by models of visual cortical areas as multisensory processors and suggestive evidence for top-down parietal (but not prefrontal) inputs that could mediate visual imagery. This is consistent with modality-independent representations accessible through both bottom-up sensory inputs and top-down processes such as visual imagery.
Krishnankutty Sathian - One of the best experts on this subject based on the ideXlab platform.
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effect of hemodynamic variability on granger Causality Analysis of fmri
NeuroImage, 2010Co-Authors: Gopikrishna Deshpande, Krishnankutty SathianAbstract:In this work, we investigated the effect of the regional variability of the hemodynamic response on the sensitivity of Granger Causality (GC) Analysis of functional magnetic resonance imaging (fMRI) data to neuronal causal influences. We simulated fMRI data by convolving a standard canonical hemodynamic response function (HRF) with local field potentials (LFPs) acquired from the macaque cortex and manipulated the causal influence and neuronal delays between the LFPs, the hemodynamic delays between the HRFs, the signal-to-noise ratio (SNR), and the sampling period (TR) to assess the effect of each of these factors on the detectability of the neuronal delays from GC Analysis of fMRI. In our first bivariate implementation, we assumed the worst-case scenario of the hemodynamic delay being at the empirical upper limit of its normal physiological range and opposing the direction of neuronal delay. We found that, in the absence of HRF confounds, even tens of milliseconds of neuronal delays can be inferred from fMRI. However, in the presence of HRF delays which opposed neuronal delays, the minimum detectable neuronal delay was hundreds of milliseconds. In our second multivariate simulation, we mimicked the real situation more closely by using a multivariate network of four time series and assumed the hemodynamic and neuronal delays to be unknown and drawn from a uniform random distribution. The resulting accuracy of detecting the correct multivariate network from fMRI was well above chance and was up to 90% with faster sampling. Generically, under all conditions, faster sampling and low measurement noise improved the sensitivity of GC Analysis of fMRI data to neuronal Causality.
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assessing and compensating for zero lag correlation effects in time lagged granger Causality Analysis of fmri
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Gopikrishna Deshpande, Krishnankutty SathianAbstract:Effective connectivity in brain networks can be studied using Granger Causality Analysis, which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger Causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this paper showed that the zero-lag correlation does “leak” into estimates of time-lagged Causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger Causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.
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effective connectivity during haptic perception a study using granger Causality Analysis of functional magnetic resonance imaging data
NeuroImage, 2008Co-Authors: Gopikrishna Deshpande, Randall Stilla, Krishnankutty SathianAbstract:Although it is accepted that visual cortical areas are recruited during touch, it remains uncertain whether this depends on top-down inputs mediating visual imagery or engagement of modality-independent representations by bottom-up somatosensory inputs. Here we addressed this by examining effective connectivity in humans during haptic perception of shape and texture with the right hand. Multivariate Granger Causality Analysis of functional magnetic resonance imaging (fMRI) data was conducted on a network of regions that were shape- or texture-selective. A novel network reduction procedure was employed to eliminate connections that did not contribute significantly to overall connectivity. Effective connectivity during haptic perception was found to involve a variety of interactions between areas generally regarded as somatosensory, multisensory, visual and motor, emphasizing flexible cooperation between different brain regions rather than rigid functional separation. The left postcentral sulcus (PCS), left precentral gyrus and right posterior insula were important sources of connections in the network. Bottom-up somatosensory inputs from the left PCS and right posterior insula fed into visual cortical areas, both the shape-selective right lateral occipital complex (LOC) and the texture-selective right medial occipital cortex (probable V2). In addition, top-down inputs from left postero-supero-medial parietal cortex influenced the right LOC. Thus, there is strong evidence for the bottom-up somatosensory inputs predicted by models of visual cortical areas as multisensory processors and suggestive evidence for top-down parietal (but not prefrontal) inputs that could mediate visual imagery. This is consistent with modality-independent representations accessible through both bottom-up sensory inputs and top-down processes such as visual imagery.
San X Liang - One of the best experts on this subject based on the ideXlab platform.
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normalized multivariate time series Causality Analysis and causal graph reconstruction
Entropy, 2021Co-Authors: San X LiangAbstract:Causality Analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing Causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.
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normalized multivariate time series Causality Analysis and causal graph reconstruction
arXiv: Artificial Intelligence, 2021Co-Authors: San X LiangAbstract:Causality Analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing Causality as real physical notion so as to formulate it from first principles, however, seems to go unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized, and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a huge challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.