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Bootstrap panel Granger-causality between government spending and revenue in the EU

Afonso, António; Rault, Christophe
Fonte: The William Davidson Institute at the University of Michigan Publicador: The William Davidson Institute at the University of Michigan
Tipo: Outros
Publicado em /02/2009 Português
Relevância na Pesquisa
46.5%
Using bootstrap panel analysis, allowing for cross-country correlation, without the need of pre-testing for unit roots, we study the causality between government revenue and spending for the EU in the period 1960-2006. Spend-and-tax causality is found for Italy, France, Spain, Greece, and Portugal, while tax-and-spend evidence is present for Germany, Belgium, Austria, Finland and the UK, and for several EU New Member States.

Assessing Thalamocortical Functional Connectivity with Granger Causality

Chen, Cheng; Maybhate, Anil; Israel, David; Thakor, Nitish V.; Jia, Xiaofeng
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.55%
Assessment of network connectivity across multiple brain regions is critical to understanding the mechanisms underlying various neurological disorders. Conventional methods for assessing dynamic interactions include cross-correlation and coherence analysis. However, these methods do not reveal the direction of information flow, which is important for studying the highly directional neurological system. Granger causality (GC) analysis can characterize the directional influences between two systems. We tested GC analysis for its capability to capture directional interactions within both simulated and in-vivo neural networks. The simulated networks consisted of Hindmarsh-Rose neurons; GC analysis was used to estimate the causal influences between two model networks. Our analysis successfully detected asymmetrical interactions between these networks (p<10−10, t-test). Next, we characterized the relationship between the “electrical synaptic strength” in the model networks and interactions estimated by GC analysis. We demonstrated the novel application of GC to monitor interactions between thalamic and cortical neurons following ischemia induced brain injury in a rat model of cardiac arrest (CA). We observed that during the post-CA acute period the GC interactions from the thalamus to the cortex were consistently higher than those from the cortex to the thalamus (1.983±0.278 times higher...

Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics

Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 30/07/2014 Português
Relevância na Pesquisa
46.55%
Granger causality (GC) is a powerful method for causal inference for time series. In general, the GC value is computed using discrete time series sampled from continuous-time processes with a certain sampling interval length τ, i.e., the GC value is a function of τ. Using the GC analysis for the topology extraction of the simplest integrate-and-fire neuronal network of two neurons, we discuss behaviors of the GC value as a function of τ, which exhibits (i) oscillations, often vanishing at certain finite sampling interval lengths, (ii) the GC vanishes linearly as one uses finer and finer sampling. We show that these sampling effects can occur in both linear and non-linear dynamics: the GC value may vanish in the presence of true causal influence or become non-zero in the absence of causal influence. Without properly taking this issue into account, GC analysis may produce unreliable conclusions about causal influence when applied to empirical data. These sampling artifacts on the GC value greatly complicate the reliability of causal inference using the GC analysis, in general, and the validity of topology reconstruction for networks, in particular. We use idealized linear models to illustrate possible mechanisms underlying these phenomena and to gain insight into the general spectral structures that give rise to these sampling effects. Finally...

Granger causality analysis reveals distinct spatio-temporal connectivity patterns in motor and perceptual visuo-spatial working memory

Protopapa, Foteini; Siettos, Constantinos I.; Evdokimidis, Ioannis; Smyrnis, Nikolaos
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 13/11/2014 Português
Relevância na Pesquisa
46.55%
We employed spectral Granger causality analysis on a full set of 56 electroencephalographic recordings acquired during the execution of either a 2D movement pointing or a perceptual (yes/no) change detection task with memory and non-memory conditions. On the basis of network characteristics across frequency bands, we provide evidence for the full dissociation of the corresponding cognitive processes. Movement-memory trial types exhibited higher degree nodes during the first 2 s of the delay period, mainly at central, left frontal and right-parietal areas. Change detection-memory trial types resulted in a three-peak temporal pattern of the total degree with higher degree nodes emerging mainly at central, right frontal, and occipital areas. Functional connectivity networks resulting from non-memory trial types were characterized by more sparse structures for both tasks. The movement-memory trial types encompassed an apparent coarse flow from frontal to parietal areas while the opposite flow from occipital, parietal to central and frontal areas was evident for the change detection-memory trial types. The differences among tasks and conditions were more profound in α (8–12 Hz) and β (12–30 Hz) and less in γ (30–45 Hz) band. Our results favor the hypothesis which considers spatial working memory as a by-product of specific mental processes that engages common brain areas under different network organizations.

Control over the strength of connections between modules: a double dissociation between stimulus format and task revealed by Granger causality mapping in fMRI

Anderson, Britt; Soliman, Sherif; O’Malley, Shannon; Danckert, James; Besner, Derek
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 27/03/2015 Português
Relevância na Pesquisa
46.55%
Drawing on theoretical and computational work with the localist dual route reading model and results from behavioral studies, Besner et al. (2011) proposed that the ability to perform tasks that require overriding stimulus-specific defaults (e.g., semantics when naming Arabic numerals, and phonology when evaluating the parity of number words) necessitate the ability to modulate the strength of connections between cognitive modules for lexical representation, semantics, and phonology on a task- and stimulus-specific basis. We used functional magnetic resonance imaging to evaluate this account by assessing changes in functional connectivity while participants performed tasks that did and did not require such stimulus-task default overrides. The occipital region showing the greatest modulation of BOLD signal strength for the two stimulus types was used as the seed region for Granger causality mapping (GCM). Our GCM analysis revealed a region of rostromedial frontal cortex with a crossover interaction. When participants performed tasks that required overriding stimulus type defaults (i.e., parity judgments of number words and naming Arabic numerals) functional connectivity between the occipital region and rostromedial frontal cortex was present. Statistically significant functional connectivity was absent when the tasks were the default for the stimulus type (i.e....

Short run and long run causality in time series: Inference

DUFOUR, Jean-Marie; PELLETIER, Denis; RENAULT, Éric
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 223846 bytes; application/pdf
Português
Relevância na Pesquisa
46.54%
We propose methods for testing hypotheses of non-causality at various horizons, as defined in Dufour and Renault (1998, Econometrica). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the U.S. economy.; Nous proposons des méthodes pour tester des hypothèses de non-causalité à différents horizons, tel que défini dans Dufour et Renault (1998, Econometrica). Nous étudions le cas des modèles VAR en détail et nous proposons des méthodes linéaires basées sur l’estimation d’autorégressions vectorielles à différents horizons. Même si les hypothèses considérées sont non linéaires, les méthodes proposées ne requièrent que des techniques de régression linéaire de même que la théorie distributionnelle asymptotique gaussienne habituelle. Dans le cas des processus intégrés...

Is there really Granger causality between energy use and output?

Stern, David I.; Bruns, Stephan B.; Gross, Christian
Fonte: Crawford School of Public Policy, The Australian National University Publicador: Crawford School of Public Policy, The Australian National University
Tipo: Working/Technical Paper Formato: 49 pages
Português
Relevância na Pesquisa
46.55%
We carry out a meta-analysis of the very large literature on Granger causality tests between energy use and economic output to determine if there is a genuine effect in this literature or whether the large number of apparently significant results is due to publication and misspecification bias. Our model extends the standard meta-regression model for detecting genuine effects using the statistical power trace in the presence of publication biases by controlling for the tendency to over-fit vector auto regression models in small samples. These over-fitted models have inflated type 1 errors. We find that models that include energy prices as a control variable find a genuine effect from output to energy use in the long-run. A genuine causal effect also seems apparent from energy to output when employment is controlled for and the Johansen procedure is used.

Disorganization of Equilibrium Directional Interactions in the Brain Motor Network of Parkinson's disease: New Insight of Resting State Analysis Using Granger Causality and Graphical Approach

Ghasemi, Mahdieh; Mahloojifar, Ali
Fonte: Medknow Publications & Media Pvt Ltd Publicador: Medknow Publications & Media Pvt Ltd
Tipo: Artigo de Revista Científica
Publicado em //2013 Português
Relevância na Pesquisa
46.55%
Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients with PD as compared with control group. rs-fMRI at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that information flow in PD is smaller than that in healthy individuals. We found that there is a balanced local network in healthy control group, including positive pair-wise cross connections between caudate and cerebellum and reciprocal connections between motor cortex and caudate in the left and right hemispheres. The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional organization of the brain in the resting state that affects the information transmission from and to other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore...

Comment on: Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance

Eichler, Michael
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 26/10/2012 Português
Relevância na Pesquisa
46.55%
We comment on a paper by Kaminski et al. (2001) and show that their claim of a relationship between the directed transfer function (DTF) and the concept of Granger causality is false.; Comment: 3 pages

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality

Sindhwani, Vikas; Quang, Minh Ha; Lozano, Aurelie C.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.55%
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. We develop a highly scalable and eigendecomposition-free algorithm that orchestrates two inexact solvers for simultaneously learning both the input and output components of separable matrix-valued kernels. As a key application enabled by our framework, we show how high-dimensional causal inference tasks can be naturally cast as sparse function estimation problems, leading to novel nonlinear extensions of a class of Graphical Granger Causality techniques. Our algorithmic developments and extensive empirical studies are complemented by theoretical analyses in terms of Rademacher generalization bounds.; Comment: 22 pages. Presentation changes; Corrections made to Theorem 2 (section 6.2) in this version

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality

Sindhwani, Vikas; Minh, Ha Quang; Lozano, Aurelie
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/08/2014 Português
Relevância na Pesquisa
46.55%
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. We develop a highly scalable and eigendecomposition-free algorithm that orchestrates two inexact solvers for simultaneously learning both the input and output components of separable matrix-valued kernels. As a key application enabled by our framework, we show how high-dimensional causal inference tasks can be naturally cast as sparse function estimation problems, leading to novel nonlinear extensions of a class of Graphical Granger Causality techniques. Our algorithmic developments and extensive empirical studies are complemented by theoretical analyses in terms of Rademacher generalization bounds.; Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)

Estimating Granger causality from Fourier and wavelet transforms of time series data

Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/11/2007 Português
Relevância na Pesquisa
46.55%
Experiments in many fields of science and engineering yield data in the form of time series. The Fourier and wavelet transform-based nonparametric methods are used widely to study the spectral characteristics of these time series data. Here, we extend the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directional influences. We illustrate the utility of the proposed methods using synthetic data from network models consisting of interacting dynamical systems.; Comment: 6 pages, 2 figures

Granger Causality and Cross Recurrence Plots in Rheochaos

Ganapathy, Rajesh; Rangarajan, Govindan; Sood, A. K.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/11/2007 Português
Relevância na Pesquisa
46.55%
Our stress relaxation measurements on wormlike micelles using a Rheo-SALS (rheology + small angle light scattering) apparatus allow simultaneous measurements of the stress and the scattered depolarised intensity. The latter is sensitive to orientational ordering of the micelles. To determine the presence of causal influences between the stress and the depolarised intensity time series, we have used the technique of linear and nonlinear Granger causality. We find there exists a feedback mechanism between the two time series and that the orientational order has a stronger causal effect on the stress than vice versa. We have also studied the phase space dynamics of the stress and the depolarised intensity time series using the recently developed technique of cross recurrence plots (CRPs). The presence of diagonal line structures in the CRPs unambiguously proves that the two time series share similar phase space dynamics.; Comment: 10 pages, 7 figures

The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach

Wilms, Ines; Gelper, Sarah; Croux, Christophe
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/08/2015 Português
Relevância na Pesquisa
46.55%
We study the predictive power of industry-specific economic sentiment indicators for future macro-economic developments. In addition to the sentiment of firms towards their own business situation, we study their sentiment with respect to the banking sector - their main credit providers. The use of industry-specific sentiment indicators results in a high-dimensional forecasting problem. To identify the most predictive industries, we present a bootstrap Granger Causality test based on the Adaptive Lasso. This test is more powerful than the standard Wald test in such high-dimensional settings. Forecast accuracy is improved by using only the most predictive industries rather than all industries.

On directed information theory and Granger causality graphs

Amblard, P. O.; Michel, O. J. J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/02/2010 Português
Relevância na Pesquisa
46.55%
Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.; Comment: accepted for publications, Journal of Computational Neuroscience

Granger Causality Stock Market Networks: Temporal Proximity and Preferential Attachment

Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/08/2014 Português
Relevância na Pesquisa
46.55%
The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, preferential attachment between stock markets exists, i.e. spillover from market j to market i is more likely if A) market j influences other markets other than i, or when B) market i is influenced by other markets other than j.; Comment: This work was supported by the Slovak Research and Development Agency under the contract No. APVV-0666-11

Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data

Fallani, Fabrizio De Vico; Corazzol, Martina; Sternberg, Jenna R.; Wyart, Claire; Chavez, Mario
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/07/2014 Português
Relevância na Pesquisa
46.55%
The recent development of genetically encoded calcium indicators enables monitoring in vivo the activity of neuronal populations. Most analysis of these calcium transients relies on linear regression analysis based on the sensory stimulus applied or the behavior observed. To estimate the basic properties of the functional neural circuitry, we propose a network-based approach based on calcium imaging recorded at single cell resolution. Differently from previous analysis based on cross-correlation, we used Granger-causality estimates to infer activity propagation between the activities of different neurons. The resulting functional networks were then modeled as directed graphs and characterized in terms of connectivity and node centralities. We applied our approach to calcium transients recorded at low frequency (4 Hz) in ventral neurons of the zebrafish spinal cord at the embryonic stage when spontaneous coiling of the tail occurs. Our analysis on population calcium imaging data revealed a strong ipsilateral connectivity and a characteristic hierarchical organization of the network hubs that supported established propagation of activity from rostral to caudal spinal cord. Our method could be used for detecting functional defects in neuronal circuitry during development and pathological conditions.

Econometric analysis of global climate change

Stern, David; Kaufmann, R K
Fonte: Pergamon-Elsevier Ltd Publicador: Pergamon-Elsevier Ltd
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.55%
This paper reports on research that applies econometric time series methods to the analysis of global climate change. The aim of this research was to test hypotheses concerning the causes of the historically observed rise in global temperatures. Longer term applications include quantification of the contribution of different forcing variables to historic warming and use of the model as a module in integrated assessment. Research to date has comprised three stages. In the first stage we used the concept of Granger causality and differences between the temperature record in the northern and southern hemispheres to investigate the causes of temperature increase. In the second stage we tested various global change time series for the presence of stochastic trends. We found that most series contain a stochastic trend with the greenhouse gas series containing I(2) stochastic trends. In the third stage we developed a structural time series to investigate some of the hypotheses suggested by the earlier stages and further tested for the presence of an I(2) trend in hemispheric temperature series. We found that the two temperature series share a common I(2) stochastic trend that may have its source in radiative forcing due to greenhouse gases. There is a second non-stationary component that appears only in the northern hemisphere and appears to be related to radiative forcing due to anthropogenic sulphur emissions.

Applying recent developments in time series econometrics to the spatial domain

Stern, David
Fonte: Blackwell Publishing Ltd Publicador: Blackwell Publishing Ltd
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.55%
This paper surveys some recent developments in time series econometrics and examines to what degree they might have useful analogs in spatial econometrics. Spatial analogs of stationary vector autoregression models might be useful in modeling groups of spatial series, but the literature on non-stationarity and cointegration does not have a useful purely spatial analog. With the exception of some special cases, pure spatial series cannot be integrated processes. However, cointegration might apply to space-time processes. Space-time cointegration and Granger causality methods are developed and applied to explaining reductions in sulfur emissions in Europe.

The causal relationship between tourism and economic growth in Malaysia: Evidence from multivariate causality tests

Kadir,Norsiah; Nayan,Sabri; Abdullah,Mat Saad
Fonte: Escola Superior de Gestão, Hotelaria e Turismo Publicador: Escola Superior de Gestão, Hotelaria e Turismo
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2010 Português
Relevância na Pesquisa
46.54%
Tourism industry and economic growth of a particular country are to some extent, interrelated. The aim of this study is to investigate the presence as well as direction of (significant) causal relationship between the Malaysian international tourism receipts and real growth in its national economy. Based on the sample period of 1994 through 2004, the data are examined from the perspective of multivariate causality procedure. Major finding of the study is twofold: First, international tourism receipts and real economic growth are found to be significantly cointegrated. Secondly, multivariate causality test based on the error correction model reveals that the Granger causality between international tourism receipts and real economic growth is unidirectional - running from real economic growth to international tourism receipts. The practical implication that could be conceived from this ‘growth-led tourism’ finding is that, as the Malaysian economy is growing, accelerated growth of socio-economic activities as well as business opportunities in its tourism-related sectors could be expected.