179,346 research outputs found
Granger causality vs. dynamic Bayesian network inference: a comparative study
No full textBackground
In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.
Results
In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better.
Conclusion
When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better
Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach
No full textBackground
Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.
Results
Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.
Conclusions
The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data
Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise
No full textWe 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, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention
Decomposing Granger causality over the spectrum.
No full textWe develop a bivariate spectral Granger-causality test that can be applied at each individual frequency of the spectrum. The spectral approach to Granger causality has the distinct advantage that it allows to disentangle (potentially) different Granger-causality relationships over different time horizons. We illustrate the usefulness of the proposed approach in the context of the predictive value of European production expectation surveys.Business surveys; Frequency; Granger causality; Production expectations; Spectral analysis; Surveys; Time; Value;
The Granger-Causality between Transportation and GDP: A Panel Data Approach
No full textThis study investigates the Granger-causality relationship between real per capita GDP and transportation of EU-15 countries using a panel data set covering the period 1970-2008. Our findings indicate that the dominant type of Granger-causality is bidirectional. Accordingly, we conclude that care must be paid in defining the dependent and independent variables when studying the relationship between transportation and income. Instances of one-way or no Granger-causality were found to correspond with countries with the lowest income per capita ranks in 1970 and/or in 2008, including Portugal, Greece and Italy. We speculate that bi-directional Granger causality between income and transportation is observed only after an economy has completed its transition in terms of economic development.Granger-causality, Transportation, Income
Testing for Granger Non-causality in Heterogeneous Panels
No full textThis paper proposes a very simple test of Granger (1969) non-causality for hetero- geneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units. First, this statistic is shown to converge sequentially to a standard normal distribution. Second, the semi- asymptotic distribution of the average statistic is characterized for a fixed T sample. A standardized statistic based on an approximation of the moments of Wald statistics is hence proposed. Third, Monte Carlo experiments show that our standardized panel statistics have very good small sample properties, even in the presence of cross-sectional dependence.Granger non-causality; Panel data; Wald Test.
Public Debt and Economic Growth: a Granger Causality Panel Data Approach
No full textThis paper analyses the Granger-causality relationship between the growth of the real GDP per capita and the public debt, here represented by the ratio of the current primary surplus/GDP and the ratio of the gross Government debt/GDP. Using OECD annual data for 20 countries between 1988 and 2001, we adapt the methodology recently applied by Erdil and Yetkiner (2008) and we conclude that there is clear Granger causality and that it is always bi-directional. In addition, our findings point to a heterogeneous behaviour across the different countries. These results have important policy implications since not only does public debt restrain economic growth, but also real GDP per capita growth influences the evolution of public debt. Key words: panel data; public debt and economic growth
FDI and trade: A Granger causality analysis in a heterogeneous panel
No full textThis paper will investigate the Granger causality between outward Foreign Direct Investment (FDI) and the exports of goods and services in 11 European countries from 1996 to 2008. Using a new method to evaluate causality in a heterogeneous panel, we find that the causal relationship from FDI to exports is homogeneous among the panel. However, we find strong evidence of a heterogeneity of the causal relationship from exports to FDI in our sample.Foreign direct investment, exports, Granger causality, heterogeneous panel
Granger causality between energy use and economic growth in France with using geostatistical models
No full textThis paper introduces a new way for investigating linear and nonlinear Granger causality between energy use and economic growth in France over the period 1960_2005 with using geostatistical models (kiriging and IDW). This approach imitates the Granger definition and structure and also, improves it to have better ability for probe nonlinear causality. Results of both VEC and Improved-VEC (with geostatistical methods) are almost same. Both show the existence of long run unidirectional causality from energy consumption to economic growth. The geostatistical analyzing shows there are some Exponential functions in VEC structure instead of linear form.Granger causality; Energy consumption; GDP; Geostatistical model; France
Foreign direct investment and corruption in developing economies: Evidence from linear and non-linear panel Granger causality tests
No full textThis paper determines the causal link between FDI and corruption in 42 developing countries using linear and non linear panel Granger causal methods over the period 1998 to 2009. The findings show that the outcome of the causal association depends on the method used. The linear panel methods revealed that the majority of the markets indicate a bidirectional causal link between FDI and corruption while in contrast, for the nonlinear tests, the link from FDI to corruption dominates.
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