538 research outputs found
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.
Arrival Time Statistics in Global Disease Spread
Metapopulation models describing cities with different populations coupled by
the travel of individuals are of great importance in the understanding of
disease spread on a large scale. An important example is the Rvachev-Longini
model [{\it Math. Biosci.} {\bf 75}, 3-22 (1985)] which is widely used in
computational epidemiology. Few analytical results are however available and in
particular little is known about paths followed by epidemics and disease
arrival times. We study the arrival time of a disease in a city as a function
of the starting seed of the epidemics. We propose an analytical Ansatz, test it
in the case of a spreading on the world wide air transportation network, and
show that it predicts accurately the arrival order of a disease in world-wide
cities
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
Impaired transmission in the corticospinal tract and gait disability in spinal cord injured persons
Rehabilitation following spinal cord injury is likely to depend on recovery of corticospinal systems. Here we investigate whether transmission in the corticospinal tract may explain foot drop (inability to dorsiflex ankle) in persons with spinal cord lesion. The study was performed in 24 persons with incomplete spinal cord lesion (C1 to L1) and 15 healthy controls. Coherence in the 10- to 20-Hz frequency band between paired tibialis anterior muscle (TA) electromyographic recordings obtained in the swing phase of walking, which was taken as a measure of motor unit synchronization. It was significantly correlated with the degree of foot drop, as measured by toe elevation and ankle angle excursion in the first part of swing. Transcranial magnetic stimulation was used to elicit motor-evoked potentials (MEPs) in the TA. The amplitude of the MEPs at rest and their latency during contraction were correlated to the degree of foot drop. Spinal cord injured participants who exhibited a large foot drop had little or no MEP at rest in the TA muscle and had little or no coherence in the same muscle during walking. Gait speed was correlated to foot drop, and was the lowest in participants with no MEP at rest. The data confirm that transmission in the corticospinal tract is of importance for lifting the foot during the swing phase of human gait
Weighted evolving networks: coupling topology and weights dynamics
We propose a model for the growth of weighted networks that couples the
establishment of new edges and vertices and the weights' dynamical evolution.
The model is based on a simple weight-driven dynamics and generates networks
exhibiting the statistical properties observed in several real-world systems.
In particular, the model yields a non-trivial time evolution of vertices'
properties and scale-free behavior for the weight, strength and degree
distributions.Comment: 4 pages, 4 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
Modeling the evolution of weighted networks
We present a general model for the growth of weighted networks in which the
structural growth is coupled with the edges' weight dynamical evolution. The
model is based on a simple weight-driven dynamics and a weights' reinforcement
mechanism coupled to the local network growth. That coupling can be generalized
in order to include the effect of additional randomness and non-linearities
which can be present in real-world networks. The model generates weighted
graphs exhibiting the statistical properties observed in several real-world
systems. In particular, the model yields a non-trivial time evolution of
vertices properties and scale-free behavior with exponents depending on the
microscopic parameters characterizing the coupling rules. Very interestingly,
the generated graphs spontaneously achieve a complex hierarchical architecture
characterized by clustering and connectivity correlations varying as a function
of the vertices' degree
Co-evolution of density and topology in a simple model of city formation
We study the influence that population density and the road network have on
each others' growth and evolution. We use a simple model of formation and
evolution of city roads which reproduces the most important empirical features
of street networks in cities. Within this framework, we explicitely introduce
the topology of the road network and analyze how it evolves and interact with
the evolution of population density. We show that accessibility issues -pushing
individuals to get closer to high centrality nodes- lead to high density
regions and the appearance of densely populated centers. In particular, this
model reproduces the empirical fact that the density profile decreases
exponentially from a core district. In this simplified model, the size of the
core district depends on the relative importance of transportation and rent
costs.Comment: 13 pages, 13 figure
Efficient routing on complex networks
In this letter, we propose a new routing strategy to improve the
transportation efficiency on complex networks. Instead of using the routing
strategy for shortest path, we give a generalized routing algorithm to find the
so-called {\it efficient path}, which considers the possible congestion in the
nodes along actual paths. Since the nodes with largest degree are very
susceptible to traffic congestion, an effective way to improve traffic and
control congestion, as our new strategy, can be as redistributing traffic load
in central nodes to other non-central nodes. Simulation results indicate that
the network capability in processing traffic is improved more than 10 times by
optimizing the efficient path, which is in good agreement with the analysis.Comment: 4 pages, 4 figure
Epidemic variability in complex networks
We study numerically the variability of the outbreak of diseases on complex
networks. We use a SI model to simulate the disease spreading at short times,
in homogeneous and in scale-free networks. In both cases, we study the effect
of initial conditions on the epidemic's dynamics and its variability. The
results display a time regime during which the prevalence exhibits a large
sensitivity to noise. We also investigate the dependence of the infection time
on nodes' degree and distance to the seed. In particular, we show that the
infection time of hubs have large fluctuations which limit their reliability as
early-detection stations. Finally, we discuss the effect of the multiplicity of
shortest paths between two nodes on the infection time. Furthermore, we
demonstrate that the existence of even longer paths reduces the average
infection time. These different results could be of use for the design of
time-dependent containment strategies
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