A Consistent Nonparametric Test for Granger Non-Causality Based on the Transfer Entropy.

To date, testing for Granger non-causality using kernel density-based nonparametric estimates of the transfer entropy has been hindered by the intractability of the asymptotic distribution of the estimators. We overcome this by shifting from the transfer entropy to its first-order Taylor expansion near the null hypothesis, which is also non-negative and zero if and only if Granger causality is absent. The estimated Taylor expansion can be expressed in terms of a U-statistic, demonstrating asymptotic normality. After studying its size and power properties numerically, the resulting test is illustrated empirically with applications to stock indices and exchange rates.

Information Cost, Memory Length and Market Instability.

In this article, we study the instability of a stock market with a modified version of Diks and Dindo's (2008) model where the market is characterized by nonlinear interactions between informed traders and uninformed traders. In the interaction of heterogeneous agents, we replace the replicator dynamics for the fractions by logistic strategy switching. This modification makes the model more suitable for describing realistic price dynamics, as well as more robust with respect to parameter changes. One goal of our paper is to use this model to explore if the arrival of new information (news) and investor behavior have an effect on market instability. A second, related, goal is to study the way markets absorb new information, especially when the market is unstable and the price is far from being fully informative. We find that the dynamics become locally unstable and prices may deviate far from the fundamental price, routing to chaos through bifurcation, with increasing information costs or decreasing memory length of the uninformed traders.

New nonparametric measures for instantaneous and granger-causality tail co-dependence.

We propose a new methodology to asses risk spillovers in a time-series framework. Firstly, we introduce an explicit nonparametric measure of cross-sectional conditional tail co-movement, which is intuitively comparable to the Conditional Value-at-Risk (CoVaR). We show that nonlinear CoVaR (NCoVaR) is able to capture even highly nonlinear dependence structures. Secondly, for the purpose of potential contagion analysis, we adapt the measure to be informative about the causality direction between the variables in the Granger causality sense. By showing that the natural estimators of the two metrics are U-statistics, we construct formal nonparametric tests for independence and Granger non-causality. Numerical simulations confirm that in common situations the nonparametric tests have better size and power properties than their parametric counterparts. The methodology is illustrated empirically by assessing risk transmissions between sovereigns and banking sectors in the euro area, which observed highly irregular co-movements between asset prices after the global financial crisis. The new measures seem to be less susceptible to these irregularities than their parametric analogues, providing a clearer overview of the underlying sovereign-bank risk feedback loops.

Functional Structure in Production Networks.

Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks within a wider space of networks that are different in nature, but similar in local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth.

Link-based influence maximization in networks of health promotion professionals.

The influence maximization problem (IMP) as classically formulated is based on the strong assumption that "chosen" nodes always adopt the new product. In this paper we propose a new influence maximization problem, referred to as the "Link-based Influence Maximization Problem" (LIM), which differs from IMP in that the decision variable of the spreader has changed from choosing an optimal seed to selecting an optimal node to influence in order to maximize the spread. Based on our proof that LIM is NP-hard with a monotonic increasing and submodular target function, we propose a greedy algorithm, GLIM, for optimizing LIM and use numerical simulation to explore the performance in terms of spread and computation time in different network types. The results indicate that the performance of LIM varies across network types. We illustrate LIM by applying it in the context of a Dutch national health promotion program for prevention of youth obesity within a network of Dutch schools. GLIM is seen to outperform the other methods in all network types at the cost of a higher computation time. These results suggests that GLIM may be utilized to increase the effectiveness of health promotion programs.

Assessment of resampling methods for causality testing: A note on the US inflation behavior.

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms.

Nonlinear analysis of the pharmacological conversion of sustained atrial fibrillation in conscious goats by the class Ic drug cibenzoline.

Methods from nonlinear dynamics were applied to test the hypothesis that the dynamics of sustained atrial fibrillation (AF) is modified by the class Ic drug cibenzoline during pharmacological conversion. The experiments were performed in conscious goats in which sustained AF was induced by continuous maintenance of AF via programmed electrical stimulation. Data were collected from electrophysiological experiments in five goats to terminate sustained AF by continuous infusion of cibenzoline. Sets of five unipolar epicardial electrograms of one minute duration were recorded from the left and right atrial free wall during sustained AF (control), and at three episodes during infusion of cibenzoline, when the mean AF interval had been prolonged to 25%, 50% and 85% with respect to control. Ventricular far-field potentials were removed from atrial electrograms by a coherent averaging procedure. Using the Grassberger-Procaccia method, the dynamics of the local atrial electrograms was investigated by estimating the (coarse-grained) correlation dimension and correlation entropy from the correlation integral. The results were related to a recently proposed classification (types I-III) of AF based on the degree of complexity of atrial activation patterns. The coarse-grained correlation dimension D(cg) and entropy K(cg) indicated that sustained AF corresponded to type II. During drug administration the coarse-grained parameters were not significantly different from control. Scaling regions in the correlation integral were observed after infusion of cibenzoline (3 out of 5 goats) suggesting that the drug introduced low-dimensional features (type I) in the dynamics of AF (correlation dimension D ranging from 2.8 to 4.4 and correlation entropy K from 1.6 to 6.2 nats/s). Sinus rhythm recorded shortly after cardioversion was very regular (D<2 and K<3 nats/s). The hypothesis that the electrograms during AF and sinus rhythm were generated by a static transformation of a linear Gaussian random process was rejected using a test for time reversibility. The nonlinear analysis revealed that cibenzoline does not significantly alter the dynamics of sustained AF during pharmacological conversion other than a slowing down of the atrial activation and a somewhat increasing global organization of the atrial activation pattern. The sudden change in the dynamical behavior at cardioversion suggests a mechanism that is reminiscent of a bifurcation. (c) 1997 American Institute of Physics.