Identification of Economic Shocks by Inequality Constraints in Bayesian Structural Vector Autoregression

Theories often make predictions about the signs of the effects of economic shocks on observable variables, thus implying inequality constraints on the parameters of a structural vector autoregression (SVAR). We introduce a new Bayesian procedure to evaluate the probabilities of such constraints, and, hence, to validate the theoretically implied economic shocks. We first estimate a SVAR, where the shocks are identified by statistical properties of the data, and subsequently label these statistically identified shocks by the Bayes factors calculated from their probabilities of satisfying given inequality constraints. In contrast to the related sign restriction approach that also makes use of theoretically implied inequality constraints, no restrictions are imposed. Hence, it is possible that only a subset or none of the theoretically implied shocks can be labelled. In the latter case, we conclude that the data do not lend support to the theory implying the signs of the effects in question. We illustrate the method by empirical applications to the crude oil market, and U.S. monetary policy.Peer reviewe

Least Dependent Component Analysis Based on Mutual Information

We propose to use precise estimators of mutual information (MI) to find least dependent components in a linearly mixed signal. On the one hand this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand it has the advantage, compared to other implementations of `independent' component analysis (ICA) some of which are based on crude approximations for MI, that the numerical values of the MI can be used for: (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output, by comparing the pairwise MIs with those of re-mixed components; (iii) clustering the output according to the residual interdependencies. For the MI estimator we use a recently proposed k-nearest neighbor based algorithm. For time sequences we combine this with delay embedding, in order to take into account non-trivial time correlations. After several tests with artificial data, we apply the resulting MILCA (Mutual Information based Least dependent Component Analysis) algorithm to a real-world dataset, the ECG of a pregnant woman. The software implementation of the MILCA algorithm is freely available at http://www.fz-juelich.de/nic/cs/softwareComment: 18 pages, 20 figures, Phys. Rev. E (in press

Unraveling the "Pressure-Effect" in Nucleation

The influence of the pressure of a chemically inert carrier-gas on the nucleation rate is one of the biggest puzzles in the research of gas-liquid nucleation. Different experiments can show a positive effect, a negative effect, or no effect at all. The same experiment may show both trends for the same substance depending on temperature, or for different substances at the same temperature. We show how this ambiguous effect naturally arises from the competition of two contributions: nonisothermal effects and pressure-volume work. Our model clarifies seemingly contradictory experimental results and quantifies the variation of the nucleation ability of a substance in the presence of an ambient gas. Our findings are corroborated by results from molecular dynamics simulation and might have important implications since nucleation in experiments, technical applications, and nature practically always occurs in the presence of an ambient gas.Comment: 4 pages, 3 figures. v2: All citations now appear correctly. v3: Updated one point in Fig.

Independent Component Analysis of Spatiotemporal Chaos

Two types of spatiotemporal chaos exhibited by ensembles of coupled nonlinear oscillators are analyzed using independent component analysis (ICA). For diffusively coupled complex Ginzburg-Landau oscillators that exhibit smooth amplitude patterns, ICA extracts localized one-humped basis vectors that reflect the characteristic hole structures of the system, and for nonlocally coupled complex Ginzburg-Landau oscillators with fractal amplitude patterns, ICA extracts localized basis vectors with characteristic gap structures. Statistics of the decomposed signals also provide insight into the complex dynamics of the spatiotemporal chaos.Comment: 5 pages, 6 figures, JPSJ Vol 74, No.

Identifying phase synchronization clusters in spatially extended dynamical systems

We investigate two recently proposed multivariate time series analysis techniques that aim at detecting phase synchronization clusters in spatially extended, nonstationary systems with regard to field applications. The starting point of both techniques is a matrix whose entries are the mean phase coherence values measured between pairs of time series. The first method is a mean field approach which allows to define the strength of participation of a subsystem in a single synchronization cluster. The second method is based on an eigenvalue decomposition from which a participation index is derived that characterizes the degree of involvement of a subsystem within multiple synchronization clusters. Simulating multiple clusters within a lattice of coupled Lorenz oscillators we explore the limitations and pitfalls of both methods and demonstrate (a) that the mean field approach is relatively robust even in configurations where the single cluster assumption is not entirely fulfilled, and (b) that the eigenvalue decomposition approach correctly identifies the simulated clusters even for low coupling strengths. Using the eigenvalue decomposition approach we studied spatiotemporal synchronization clusters in long-lasting multichannel EEG recordings from epilepsy patients and obtained results that fully confirm findings from well established neurophysiological examination techniques. Multivariate time series analysis methods such as synchronization cluster analysis that account for nonlinearities in the data are expected to provide complementary information which allows to gain deeper insights into the collective dynamics of spatially extended complex systems

Grazing in a megagrazer-dominated savanna does not reduce soil carbon stocks, even at high intensities

Recent studies suggest that wild animals can promote ecosystem carbon sinks through their impacts on vegetation and soils. However, livestock studies show that intense levels of grazing reduce soil organic carbon (SOC), leading to concerns that rewilding with large grazers may compromise ecosystem carbon storage. Furthermore, wild grazers can both limit and promote woody plant recruitment and survival on savanna grasslands, with both positive and negative impacts on SOC, depending on the rainfall and soil texture contexts. We used grazing lawns in one of the few African protected savannas that are still dominated by megagrazers (> 1000 kg), namely white rhinoceros Ceratotherium simum, as a model to study the impact of prolonged and intense wild grazing on SOC stocks. We contrasted SOC stocks between patches of varying grazing intensity and woody plant encroachment in sites across different rhino habitat types. We found no differences in SOC stocks between the most- and least grazed plots in any of the habitats. Intermediately grazed plots, however, had higher SOC stocks in the top 5 cm compared to most and least grazed plots, but only in the closed-canopy woodland habitat and not in the open habitats. Importantly, we found no evidence to support the hypothesis that wild grazing reduces SOC, even at high grazing intensities by the world's largest megagrazer. Compared to the non-encroached reference plots, woody encroached plots had higher SOC stocks in soils with low clay content and lower SOC stocks in soils with high clay content, although only in the top 5 cm. Accordingly, our study highlights that wild grazers may influence SOC indirectly through their impact on tree-grass ratios in grassy ecosystems. Our study thus provides important insights for future natural climate solutions that focus on wild grazer conservation and restoration.Keywords: fire, grazing impact, rewilding, soil carbon, white rhinoceros, woody encroachmen

Microdebrider is less aerosol-generating than CO2 laser and cold instruments in microlaryngoscopy

Objective COVID-19 spreads through aerosols produced in coughing, talking, exhalation, and also in some surgical procedures. Use of CO2 laser in laryngeal surgery has been observed to generate aerosols, however, other techniques, such cold dissection and microdebrider, have not been sufficiently investigated. We aimed to assess whether aerosol generation occurs during laryngeal operations and the effect of different instruments on aerosol production. Methods We measured particle concentration generated during surgeries with an Optical Particle Sizer. Cough data collected from volunteers and aerosol concentration of an empty operating room served as references. Aerosol concentrations when using different techniques and equipment were compared with references as well as with each other. Results Thirteen laryngological surgeries were evaluated. The highest total aerosol concentrations were observed when using CO2 laser and these were significantly higher than the concentrations when using microdebrider or cold dissection (p < 0.0001, p < 0.0001) or in the background or during coughing (p < 0.0001, p < 0.0001). In contrast, neither microdebrider nor cold dissection produced significant concentrations of aerosol compared with coughing (p = 0.146, p = 0.753). In comparing all three techniques, microdebrider produced the least aerosol particles. Conclusions Microdebrider and cold dissection can be regarded as aerosol-generating relative to background reference concentrations, but they should not be considered as high-risk aerosol-generating procedures, as the concentrations are low and do not exceed those of coughing. A step-down algorithm from CO2 laser to cold instruments and microdebrider is recommended to lower the risk of airborne infections among medical staff.Peer reviewe