560 research outputs found
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.
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
Reply to "Comment on 'Universal Behavior of Load Distribution in Scale-Free Networks'"
Reply to "Comment on 'Universal Behavior of Load Distribution in Scale-Free
Networks.'"Comment: 1 page, 1 figur
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
Modeling urban street patterns
Urban streets patterns form planar networks whose empirical properties cannot
be accounted for by simple models such as regular grids or Voronoi
tesselations. Striking statistical regularities across different cities have
been recently empirically found, suggesting that a general and
details-independent mechanism may be in action. We propose a simple model based
on a local optimization process combined with ideas previously proposed in
studies of leaf pattern formation. The statistical properties of this model are
in good agreement with the observed empirical patterns. Our results thus
suggests that in the absence of a global design strategy, the evolution of many
different transportation networks indeed follow a simple universal mechanism.Comment: 4 pages, 5 figures, final version published in PR
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
Analytic solution of a static scale-free network model
We present a detailed analytical study of a paradigmatic scale-free network
model, the Static Model. Analytical expressions for its main properties are
derived by using the hidden variables formalism. We map the model into a
canonic hidden variables one, and solve the latter. The good agreement between
our predictions and extensive simulations of the original model suggests that
the mapping is exact in the infinite network size limit. One of the most
remarkable findings of this study is the presence of relevant disassortative
correlations, which are induced by the physical condition of absence of self
and multiple connections.Comment: 8 pages, 4 figure
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
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
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