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A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets

FUJITA, Andre; KOJIMA, Kaname; PATRIOTA, Alexandre G.; SATO, Joao R.; SEVERINO, Patricia; MIYANO, Satoru
Fonte: OXFORD UNIV PRESS Publicador: OXFORD UNIV PRESS
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.78%
We propose a likelihood ratio test ( LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrapbased approach. LRT is shown to be significantly faster and statistically powerful even within non- Normal distributions. An R package named gGranger containing an implementation for both Granger causality identification tests is also provided.; RIKEN, Japan; RIKEN, Japan; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP, Brazil

Avaliação de dados funcionais em repouso do cérebro normal: causalidade de Granger

Borralho, Ana Catarina Costeira
Fonte: Escola Superior de Tecnologia da Saúde de Lisboa Publicador: Escola Superior de Tecnologia da Saúde de Lisboa
Tipo: Dissertação de Mestrado
Publicado em //2012 Português
Relevância na Pesquisa
46.8%
Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Ressonância Magnética; O objectivo deste projecto prende-se com a intenção de determinar os coeficientes de conectividade efectiva num grupo de indivíduos saudáveis, através da análise da Causalidade de Granger. Em particular, pretende-se identificar o fluxo de informação entre duas redes neuronais específicas, a DMN e a TEN. Para além destas duas estruturas também se propôs a avaliação de outras redes que nos permitem avaliar aspectos motores (rede Sensório-Motora), processamento de dados visuais (rede visual), funções executivas (rede da atenção), emoções (cingulado anterior) e bem como a tomada de decisões (rede da Ínsula). Foi realizada uma aquisição de RMf em repouso usando a técnica BOLD. Foram estudados 27 indivíduos, 24 saudáveis e 3 doentes com epilepsia pós-traumática. Este grupo de doentes foi estudado de forma preliminar para identificação de alterações a nível da conectividade efectiva das redes mencionadas. Foram realizadas análises ROI-Wise, entre cada par de redes distintas e Voxel-Wise, onde se avaliou a causalidade de um sinal funcional médio numa determinada rede em relação a todo o encéfalo usando uma estratégia de análise voxel a voxel. Estas análises permitiram determinar relações causais entre as redes e diferenciar indivíduos saudáveis e doentes. Observou-se que o Cingulado Anterior dirige os processos cognitivos da atenção...

Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality

Brovelli, Andrea; Ding, Mingzhou; Ledberg, Anders; Chen, Yonghong; Nakamura, Richard; Bressler, Steven L.
Fonte: National Academy of Sciences Publicador: National Academy of Sciences
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.83%
Previous studies have shown that synchronized beta frequency (14-30 Hz) oscillations in the primary motor cortex are involved in maintaining steady contractions of contralateral arm and hand muscles. However, little is known about the role of postcentral cortical areas in motor maintenance and their patterns of interaction with motor cortex. We investigated the functional relations of beta-synchronized neuronal assemblies in pre- and postcentral areas of two monkeys as they pressed a hand lever during the wait period of a visual discrimination task. By using power and coherence spectral analysis, we identified a beta-synchronized large-scale network linking pre- and postcentral areas. We then used Granger causality spectra to measure directional influences among recording sites. In both monkeys, strong Granger causal influences were observed from primary somatosensory cortex to both motor cortex and inferior posterior parietal cortex, with the latter area also exerting Granger causal influences on motor cortex. Granger causal influences from motor cortex to postcentral sites, however, were weak in one monkey and not observed in the other. These results are the first, to our knowledge, to demonstrate in awake monkeys that synchronized beta oscillations bind multiple sensorimotor areas into a large-scale network during motor maintenance behavior and carry Granger causal influences from primary somatosensory and inferior posterior parietal cortices to motor cortex.

Estimating Granger causality after stimulus onset: A cautionary note

Wang, Xue; Chen, Yonghong; Ding, Mingzhou
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.78%
How the brain processes sensory input to produce goal-oriented behavior is not well-understood. Advanced data acquisition technology in conjunction with novel statistical methods holds the key to future progress in this area. Recent studies have applied Granger causality to multivariate population recordings such as local field potential (LFP) or electroencephalography (EEG) in event-related paradigms. The aim is to reveal the detailed time course of stimulus-elicited information transaction among various sensory and motor cortices. Presently, interdependency measures like coherence and Granger causality are calculated on ongoing brain activity obtained by removing the average event-related potential (AERP) from each trial. In this paper we point out the pitfalls of this approach in light of the inevitable occurrence of trial-to-trial variability of event-related potentials in both amplitudes and latencies. Numerical simulations and experimental examples are used to illustrate the ideas. Special emphasis is placed on the important role played by single trial analysis of event-related potentials in experimentally establishing the main conclusion.

A comparison of Granger causality and coherency in fMRI-based analysis of the motor system

Kayser, Andrew S.; Sun, Felice T.; D’Esposito, Mark
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /11/2009 Português
Relevância na Pesquisa
46.78%
The ability of functional MRI to acquire data from multiple brain areas has spurred developments not only in voxel-by-voxel analyses, but also in multivariate techniques critical to quantifying the interactions between brain areas. As the number of multivariate techniques multiplies, however, few studies in any modality have directly compared different connectivity measures, and fewer still have done so in the context of well-characterized neural systems. To focus specifically on the temporal dimension of interactions between brain regions, we compared Granger causality and coherency (Sun et.al., 2004, 2005) in a well-studied motor system (1) to gain further insight into the convergent and divergent results expected from each technique, and (2) to investigate the leading and lagging influences between motor areas as subjects performed a motor task in which they produced different learned series of eight button presses. We found that these analyses gave convergent but not identical results: both techniques, for example, suggested an anterior-to-posterior temporal gradient of activity from supplemental motor area through premotor and motor cortices to the posterior parietal cortex, but the techniques were differentially sensitive to the coupling strength between areas. We also found practical reasons that might argue for the use of one technique over another in different experimental situations. Ultimately...

Dissociating the Contributions of Independent Corticostriatal Systems to Visual Categorization Learning Through the Use of Reinforcement Learning Modeling and Granger Causality Modeling

Seger, Carol A.; Peterson, Erik J.; Cincotta, Corinna M.; Lopez-Paniagua, Dan; Anderson, Charles W.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.78%
We dissociated the contributions to learning of four corticostriatal “loops” (interacting striatal and cortical regions): motor (putamen and motor cortex), visual (posterior caudate and visual cortex), executive (anterior caudate and prefrontal cortex), and motivational (ventral striatum and ventromedial frontal cortex). Subjects learned to categorize individual repeated images into one of two arbitrary categories via trial and error. We identified (1) Regions sensitive to correct categorization, categorization learning, and feedback valence (2) Regions sensitive to prediction error (violation of feedback expectancy) and reward prediction (expected feedback associated with category response), using reinforcement learning modeling and (3) Directed influences between regions using Granger causality modeling. Each loop showed a unique pattern of sensitivity to each of these factors. Both the motor and visual loops were involved in acquisition of categorization ability: activity during correct categorization increased across learning, and was sensitive to reward prediction. However, the posterior caudate received directed influence from visual cortex, whereas the putamen exerted directed influence on motor cortex. The motivational and executive loops were involved in feedback processing: both regions were sensitive to feedback valence...

Recurrent Activity in Higher Order, Modality Non-Specific Brain Regions: A Granger Causality Analysis of Autobiographic Memory Retrieval

Lou, Hans C.; Joensson, Morten; Biermann-Ruben, Katja; Schnitzler, Alfons; Østergaard, Leif; Kjaer, Troels W.; Gross, Joachim
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 27/07/2011 Português
Relevância na Pesquisa
46.78%
It has been proposed that the workings of the brain are mainly intrinsically generated recurrent neuronal activity, with sensory inputs as modifiers of such activity in both sensory and higher order modality non-specific regions. This is supported by the demonstration of recurrent neuronal activity in the visual system as a response to visual stimulation. In contrast recurrent activity has never been demonstrated before in higher order modality non-specific regions. Using magneto-encephalography and Granger causality analysis, we tested in a paralimbic network the hypothesis that stimulation may enhance causal recurrent interaction between higher-order, modality non-specific regions. The network includes anterior cingulate/medial prefrontal and posterior cingulate/medial parietal cortices together with pulvinar thalami, a network known to be effective in autobiographic memory retrieval and self-awareness. Autobiographic memory retrieval of previous personal judgments of visually presented words was used as stimuli. It is demonstrated that the prestimulus condition is characterized by causal, recurrent oscillations which are maximal in the lower gamma range. When retrieving previous judgments of visually presented adjectives, this activity is dramatically increased during the stimulus task as ascertained by Granger causality analysis. Our results confirm the hypothesis that stimulation may enhance causal interaction between higher order...

Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

Doerfler, Hannes; Lyon, David; Nägele, Thomas; Sun, Xiaoliang; Fragner, Lena; Hadacek, Franz; Egelhofer, Volker; Weckwerth, Wolfram
Fonte: Springer US Publicador: Springer US
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.78%
Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality...

Is Granger Causality a Viable Technique for Analyzing fMRI Data?

Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 04/07/2013 Português
Relevância na Pesquisa
46.78%
Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI) data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a) latency differences in hemodynamic response function (HRF) across different brain regions, (b) low-sampling rates, and (c) noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC) between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1) GC following HRF convolution is a monotonically increasing function of neural GC; (2) this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3) although the detectability of monotonicity declined due to the presence of HRF latency differences...

Attention-Dependent Modulation of Cortical Taste Circuits Revealed by Granger Causality with Signal-Dependent Noise

Luo, Qiang; Ge, Tian; Grabenhorst, Fabian; Feng, Jianfeng; Rolls, Edmund T.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.8%
We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition...

Combining independent component analysis and Granger causality to investigate brain network dynamics with fNIRS measurements

Yuan, Zhen
Fonte: Optical Society of America Publicador: Optical Society of America
Tipo: Artigo de Revista Científica
Publicado em 25/10/2013 Português
Relevância na Pesquisa
46.78%
In this study a new strategy that combines Granger causality mapping (GCM) and independent component analysis (ICA) is proposed to reveal complex neural network dynamics underlying cognitive processes using functional near infrared spectroscopy (fNIRS) measurements. The GCM-ICA algorithm implements the following two procedures: (i) extraction of the region of interests (ROIs) of cortical activations by ICA, and (ii) estimation of the direct causal influences in local brain networks using Granger causality among voxels of ROIs. Our results show that the use of GCM in conjunction with ICA is able to effectively identify the directional brain network dynamics in time-frequency domain based on fNIRS recordings.

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Kong, Wanzeng; Lin, Weicheng; Babiloni, Fabio; Hu, Sanqing; Borghini, Gianluca
Fonte: MDPI Publicador: MDPI
Tipo: Artigo de Revista Científica
Publicado em 05/08/2015 Português
Relevância na Pesquisa
46.78%
Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies.

Nonlinear Causality Testing with Stepwise Multivariate Filtering

BEKIROS, Stelios D.
Fonte: Instituto Universitário Europeu Publicador: Instituto Universitário Europeu
Tipo: Trabalho em Andamento Formato: application/pdf; digital
Português
Relevância na Pesquisa
46.8%
This study explores the direction and nature of causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). These are the most liquid and widely traded currency pairs in the world and make up about 90% of total Forex trading worldwide. The data covers the period 3/20/1987-11/14/2007, including the Asian crisis, the dot-com bubble and the period just before the outbreak of the US subprime crisis. The objective of the paper is to test for the existence of both linear and nonlinear causal relationships among these currency markets. The modified Baek-Brock test for nonlinear non-causality is applied on the currency return time series as well as the linear Granger test. Further to the classical pairwise analysis causality testing is conducted in a multivariate formulation, to correct for the effects of the other variables. A new stepwise multivariate filtering approach is implemented. To check if any of the observed causality is strictly nonlinear, the nonlinear causal relationships of VAR/VECM filtered residuals are also examined. Finally, the hypothesis of nonlinear non-causality is investigated after controlling for conditional heteroskedasticity in the data using GARCH-BEKK...

The reaction of stock market returns to anticipated unemployment

Gonzalo, Jesús; Taamouti, Abderrahim
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper Formato: application/pdf
Publicado em /07/2011 Português
Relevância na Pesquisa
46.78%
We empirically investigate the short-run impact of anticipated and unanticipated unemployment rates on stock prices. We particularly examine the nonlinearity in stock market’s reaction to unemployment rate and study the effect at each individual point (quantile) of stock return distribution. Using nonparametric Granger causality and quantile regression based tests, we find that, contrary to the general findings in the literature, only anticipated unemployment rate has a strong impact on stock prices. Quantile regression analysis shows that the causal effects of anticipated unemployment rate on stock return are usually heterogeneous across quantiles. For quantile range [0.35, 0.80], an increase in the anticipated unemployment rate leads to an increase in the stock market price. For the other quantiles the impact is statistically insignificant. Thus, an increase in the anticipated unemployment rate is in general a good news for stock prices. Finally, we offer a reasonable explanation of why unemployment rate should affect stock prices and how it affects them. Using Fisher and Phillips curve equations, we show that high unemployment rate is followed by monetary policy action of Federal Reserve (Fed). When unemployment rate is high, the Fed decreases the interest rate...

The reaction of stock market returns to anticipated unemployment

Gonzalo, Jesús; Taamouti, Abderrahim
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper Formato: application/pdf
Publicado em 06/07/2012 Português
Relevância na Pesquisa
46.78%
We empirically investigate the short-run impact of anticipated and unanticipated unemployment rates on stock prices. We particularly examine the nonlinearity in stock market's reaction to unemployment rate and study the effect at each individual point (quantile) of stock return distribution. Using nonparametric Granger causality and quantile regression based tests, we find that, contrary to the general findings in the literature, only anticipated unemployment rate has a strong impact on stock prices. Quantile regression analysis shows that the causal effects of anticipated unemployment rate on stock return are usually heterogeneous across quantiles. For quantile range (0.35, 0.80), an increase in the anticipated unemployment rate leads to an increase in the stock market price. For the other quantiles the impact is statistically insignificant. Thus, an increase in the anticipated unemployment rate is in general good news for stock prices. Finally, we offer a reasonable explanation of why unemployment rate should affect stock prices and how it affects them. Using Fisher and Phillips curve equations, we show that high unemployment rate is followed by monetary policy action of Federal Reserve (Fed). When unemployment rate is high, the Fed decreases the interest rate...

Robust Non-linear Wiener-Granger Causality For Large High-dimensional Data

Jafari-Mamaghani, Mehrdad
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/10/2015 Português
Relevância na Pesquisa
46.78%
Wiener-Granger causality is a widely used framework of causal analysis for temporally resolved events. We introduce a new measure of Wiener-Granger causality based on kernelization of partial canonical correlation analysis with specific advantages in the context of large high-dimensional data. The introduced measure is able to detect non-linear and non-monotonous signals, is designed to be immune to noise, and offers tunability in terms of computational complexity in its estimations. Furthermore, we show that, under specified conditions, the introduced measure can be regarded as an estimate of conditional mutual information (transfer entropy). The functionality of this measure is assessed using comparative simulations where it outperforms other existing methods. The paper is concluded with an application to climatological data.

Neural Networks with Non-Uniform Embedding and Explicit Validation Phase to Assess Granger Causality

Montalto, Alessandro; Stramaglia, Sebastiano; Faes, Luca; Tessitore, Giovanni; Prevete, Roberto; Marinazzo, Daniele
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.78%
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments performed will show that the method presented in this work can detect the correct dynamical information flows occurring in a system of time series. Additionally we adopt a non-uniform embedding framework according to which only the past states that actually help the prediction are entered into the model, improving the prediction and avoiding the risk of overfitting. This method also leads to a further improvement with respect to traditional Granger causality approaches when redundant variables (i.e. variables sharing the same information about the future of the system) are involved. Neural networks are also able to recognize dynamics in data sets completely different from the ones used during the training phase.

Mitigating the effects of measurement noise on Granger causality

Nalatore, Hariharan; Rangarajan, Govindan; Ding, Mingzhou
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/11/2007 Português
Relevância na Pesquisa
46.84%
Computing Granger causal relations among bivariate experimentally observed time series has received increasing attention over the past few years. Such causal relations, if correctly estimated, can yield significant insights into the dynamical organization of the system being investigated. Since experimental measurements are inevitably contaminated by noise, it is thus important to understand the effects of such noise on Granger causality estimation. The first goal of this paper is to provide an analytical and numerical analysis of this problem. Specifically, we show that, due to noise contamination, (1) spurious causality between two measured variables can arise and (2) true causality can be suppressed. The second goal of the paper is to provide a denoising strategy to mitigate this problem. Specifically, we propose a denoising algorithm based on the combined use of the Kalman filter theory and the Expectation-Maximization (EM) algorithm. Numerical examples are used to demonstrate the effectiveness of the denoising approach.; Comment: 16 pages, 7 figures

Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model

Siggiridou, Elsa; Kugiumtzis, Dimitris
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 11/11/2015 Português
Relevância na Pesquisa
46.84%
Granger causality has been used for the investigation of the inter-dependence structure of the underlying systems of multi-variate time series. In particular, the direct causal effects are commonly estimated by the conditional Granger causality index (CGCI). In the presence of many observed variables and relatively short time series, CGCI may fail because it is based on vector autoregressive models (VAR) involving a large number of coefficients to be estimated. In this work, the VAR is restricted by a scheme that modifies the recently developed method of backward-in-time selection (BTS) of the lagged variables and the CGCI is combined with BTS. Further, the proposed approach is compared favorably to other restricted VAR representations, such as the top-down strategy, the bottom-up strategy, and the least absolute shrinkage and selection operator (LASSO), in terms of sensitivity and specificity of CGCI. This is shown by using simulations of linear and nonlinear, low and high-dimensional systems and different time series lengths. For nonlinear systems, CGCI from the restricted VAR representations are compared with analogous nonlinear causality indices. Further, CGCI in conjunction with BTS and other restricted VAR representations is applied to multi-channel scalp electroencephalogram (EEG) recordings of epileptic patients containing epileptiform discharges. CGCI on the restricted VAR...

Real Exchange Rate and Foreign Direct Investment in Sub-Saharan Africa: Some Empirical Results

Ogun,Oluremi; Egwaikhide,Festus O.; Ogunleye,Eric K.
Fonte: Centro de Investigación y Docencia Económicas Publicador: Centro de Investigación y Docencia Económicas
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2012 Português
Relevância na Pesquisa
46.84%
Despite the well acknowledged importance of foreign direct investment (FDI) and efforts of sub-Sahara African (SSA) countries at attracting it, the region remains the least destination for FDI globally. Of course, several studies have endeavored to examine the determinants of FDI in this region. This study contributes to the literature by examining a possible determinant of FDI that has received less attention in the literature: real exchange rate (RER) movements. This paper examines this relationship with a view to determining the extent to which real exchange rate movements stifle FDI inflows in selected SSA countries, employing the Granger causality and simultaneous estimation techniques. The use of simultaneous equation is informed by the theoretical and empirical inconclusiveness on the relationship between movements in RER and FDI. The Granger Causality test further provides insight on the causal direction of the variables. Whereas the causality tests suggest statistical dependence between RER movements and FDI for a few of the countries, the regression analyses show a statistically significant relationship between these variables. While the inclusion of pre-reform period in the study may have contributed to these results, the general picture emerging is that FDI flows are sensitive to REER movements in SSA.