Global Imbalances and Imported Disinflation in the Euro Area

We estimate a medium-scale DSGE model for the euro area in an open economy framework. The model includes structural trends on all variables, which allow us to estimate on gross data. We first provide a theoretical balanced growth path consistent with permanent productivity shocks, inflation target changes, and permanent shocks to the openness of the economies. We then define the cycle as the gap between this sustainable trajectory and the gross data, thus our model properly deals with deviations of the trade balance. Finally, we find persistent and strong effects from the asymmetric increase of euro area imports during the last ten years on domestic inflation. From the first quarter of 2000 to the last quarter of 2008, we estimate the contribution of the imbalanced development of international trade on euro area inflation to an average of -0.7%, and on the 3-Month interest rate to an average of -1.4%.Global Imbalances, Disinflation, Business Fluctuations, Open Economy Macroeconomics.

State-Dependent Probability Distributions in Non Linear Rational Expectations Models

In this paper, we provide solution methods for non-linear rational expectations models in which regime-switching or the shocks themselves may be "endogenous", i.e. follow state-dependent probability distributions. We use the perturbation approach to find determinacy conditions, i.e. conditions for the existence of a unique stable equilibrium. We show that these conditions directly follow from the corresponding conditions in the exogenous regime-switching model. Whereas these conditions are difficult to check in the general case, we provide for easily verifiable and sufficient determinacy conditions and first-order approximation of the solution for purely forward-looking models. Finally, we illustrate our results with a Fisherian model of inflation determination in which the monetary policy rule may change across regimes according to a state-dependent transition probability matrix.Perturbation methods, monetary policy, indeterminacy, regime switching, DSGE.

Trends and Cycles : an Historical Review of the Euro Area.

We analyze the euro area business cycle in a medium scale DSGE model where we assume two stochastic trends: one on total factor productivity and one on the inflation target of the central bank. To justify our choice of integrated trends, we test alternative specifications for both of them. We do so, estimating trends together with the model's structural parameters, to prevent estimation biases. In our estimates, business cycle fluctuations are dominated by investment specific shocks and preference shocks of households. Our results cast doubts on the view that cost push shocks dominate economic fluctuations in DSGE models and show that productivity shocks drive fluctuations on a longer term. As a conclusion, we present our estimation's historical reading of the business cycle in the euro area. This estimation gives credible explanations of major economic events since 1985.New Keynesian model, Business Cycle, Bayesian estimation.

Complete S-matrix in a microwave cavity at room temperature

We experimentally study the widths of resonances in a two-dimensional microwave cavity at room temperature. By developing a model for the coupling antennas, we are able to discriminate their contribution from those of ohmic losses to the broadening of resonances. Concerning ohmic losses, we experimentally put to evidence two mechanisms: damping along propagation and absorption at the contour, the latter being responsible for variations of widths from mode to mode due to its dependence on the spatial distribution of the field at the contour. A theory, based on an S-matrix formalism, is given for these variations. It is successfully validated through measurements of several hundreds of resonances in a rectangular cavity.Comment: submitted to PR

A New Methodology for Generalizing Unweighted Network Measures

Several important complex network measures that helped discovering common patterns across real-world networks ignore edge weights, an important information in real-world networks. We propose a new methodology for generalizing measures of unweighted networks through a generalization of the cardinality concept of a set of weights. The key observation here is that many measures of unweighted networks use the cardinality (the size) of some subset of edges in their computation. For example, the node degree is the number of edges incident to a node. We define the effective cardinality, a new metric that quantifies how many edges are effectively being used, assuming that an edge's weight reflects the amount of interaction across that edge. We prove that a generalized measure, using our method, reduces to the original unweighted measure if there is no disparity between weights, which ensures that the laws that govern the original unweighted measure will also govern the generalized measure when the weights are equal. We also prove that our generalization ensures a partial ordering (among sets of weighted edges) that is consistent with the original unweighted measure, unlike previously developed generalizations. We illustrate the applicability of our method by generalizing four unweighted network measures. As a case study, we analyze four real-world weighted networks using our generalized degree and clustering coefficient. The analysis shows that the generalized degree distribution is consistent with the power-law hypothesis but with steeper decline and that there is a common pattern governing the ratio between the generalized degree and the traditional degree. The analysis also shows that nodes with more uniform weights tend to cluster with nodes that also have more uniform weights among themselves.Comment: 23 pages, 10 figure

Social inertia in collaboration networks

This work is a study of the properties of collaboration networks employing the formalism of weighted graphs to represent their one-mode projection. The weight of the edges is directly the number of times that a partnership has been repeated. This representation allows us to define the concept of "social inertia" that measures the tendency of authors to keep on collaborating with previous partners. We use a collection of empirical datasets to analyze several aspects of the social inertia: 1) its probability distribution, 2) its correlation with other properties, and 3) the correlations of the inertia between neighbors in the network. We also contrast these empirical results with the predictions of a recently proposed theoretical model for the growth of collaboration networks.Comment: 7 pages, 5 figure

Vulnerability of weighted networks

In real networks complex topological features are often associated with a diversity of interactions as measured by the weights of the links. Moreover, spatial constraints may as well play an important role, resulting in a complex interplay between topology, weight, and geography. In order to study the vulnerability of such networks to intentional attacks, these attributes must be therefore considered along with the topological quantities. In order to tackle this issue, we consider the case of the world-wide airport network, which is a weighted heterogeneous network whose evolution and structure are influenced by traffic and geographical constraints. We first characterize relevant topological and weighted centrality measures and then use these quantities as selection criteria for the removal of vertices. We consider different attack strategies and different measures of the damage achieved in the network. The analysis of weighted properties shows that centrality driven attacks are capable to shatter the network's communication or transport properties even at very low level of damage in the connectivity pattern. The inclusion of weight and traffic therefore provides evidence for the extreme vulnerability of complex networks to any targeted strategy and need to be considered as key features in the finding and development of defensive strategies

Head-to-head comparison of 10 plasma phospho-tau assays in prodromal Alzheimer\u27s disease

Plasma phospho-tau (p-tau) species have emerged as the most promising blood-based biomarkers of Alzheimer\u27s disease. Here, we performed a head-to-head comparison of p-tau181, p-tau217 and p-tau231 measured using 10 assays to detect abnormal brain amyloid-β (Aβ) status and predict future progression to Alzheimer\u27s dementia. The study included 135 patients with baseline diagnosis of mild cognitive impairment (mean age 72.4 years; 60.7% women) who were followed for an average of 4.9 years. Seventy-one participants had abnormal Aβ-status (i.e. abnormal CSF Aβ42/40) at baseline; and 45 of these Aβ-positive participants progressed to Alzheimer\u27s dementia during follow-up. P-tau concentrations were determined in baseline plasma and CSF. P-tau217 and p-tau181 were both measured using immunoassays developed by Lilly Research Laboratories (Lilly) and mass spectrometry assays developed at Washington University (WashU). P-tau217 was also analysed using Simoa immunoassay developed by Janssen Research and Development (Janss). P-tau181 was measured using Simoa immunoassay from ADxNeurosciences (ADx), Lumipulse immunoassay from Fujirebio (Fuji) and Splex immunoassay from Mesoscale Discovery (Splex). Both p-tau181 and p-tau231 were quantified using Simoa immunoassay developed at the University of Gothenburg (UGOT). We found that the mass spectrometry-based p-tau217 (p-tau217WashU) exhibited significantly better performance than all other plasma p-tau biomarkers when detecting abnormal Aβ status [area under curve (AUC) = 0.947; Pdiff \u3c 0.015] or progression to Alzheimer\u27s dementia (AUC = 0.932; Pdiff \u3c 0.027). Among immunoassays, p-tau217Lilly had the highest AUCs (0.886-0.889), which was not significantly different from the AUCs of p-tau217Janss, p-tau181ADx and p-tau181WashU (AUCrange 0.835-0.872; Pdiff \u3e 0.09), but higher compared with AUC of p-tau231UGOT, p-tau181Lilly, p-tau181UGOT, p-tau181Fuji and p-tau181Splex (AUCrange 0.642-0.813; Pdiff ≤ 0.029). Correlations between plasma and CSF values were strongest for p-tau217WashU (R = 0.891) followed by p-tau217Lilly (R = 0.755; Pdiff = 0.003 versus p-tau217WashU) and weak to moderate for the rest of the p-tau biomarkers (Rrange 0.320-0.669). In conclusion, our findings suggest that among all tested plasma p-tau assays, mass spectrometry-based measures of p-tau217 perform best when identifying mild cognitive impairment patients with abnormal brain Aβ or those who will subsequently progress to Alzheimer\u27s dementia. Several other assays (p-tau217Lilly, p-tau217Janss, p-tau181ADx and p-tau181WashU) showed relatively high and consistent accuracy across both outcomes. The results further indicate that the highest performing assays have performance metrics that rival the gold standards of Aβ-PET and CSF. If further validated, our findings will have significant impacts in diagnosis, screening and treatment for Alzheimer\u27s dementia in the future

Velocity and hierarchical spread of epidemic outbreaks in scale-free networks

We study the effect of the connectivity pattern of complex networks on the propagation dynamics of epidemics. The growth time scale of outbreaks is inversely proportional to the network degree fluctuations, signaling that epidemics spread almost instantaneously in networks with scale-free degree distributions. This feature is associated with an epidemic propagation that follows a precise hierarchical dynamics. Once the highly connected hubs are reached, the infection pervades the network in a progressive cascade across smaller degree classes. The present results are relevant for the development of adaptive containment strategies.Comment: 4 pages, 4 figures, final versio