658 research outputs found
Sparse Bayesian vector autoregressions in huge dimensions
We develop a Bayesian vector autoregressive (VAR) model with multivariate
stochastic volatility that is capable of handling vast dimensional information
sets. Three features are introduced to permit reliable estimation of the model.
First, we assume that the reduced-form errors in the VAR feature a factor
stochastic volatility structure, allowing for conditional equation-by-equation
estimation. Second, we apply recently developed global-local shrinkage priors
to the VAR coefficients to cure the curse of dimensionality. Third, we utilize
recent innovations to efficiently sample from high-dimensional multivariate
Gaussian distributions. This makes simulation-based fully Bayesian inference
feasible when the dimensionality is large but the time series length is
moderate. We demonstrate the merits of our approach in an extensive simulation
study and apply the model to US macroeconomic data to evaluate its forecasting
capabilities
Polymer adsorption on heterogeneous surfaces
The adsorption of a single ideal polymer chain on energetically heterogeneous
and rough surfaces is investigated using a variational procedure introduced by
Garel and Orland (Phys. Rev. B 55 (1997), 226). The mean polymer size is
calculated perpendicular and parallel to the surface and is compared to the
Gaussian conformation and to the results for polymers at flat and energetically
homogeneous surfaces. The disorder-induced enhancement of adsorption is
confirmed and is shown to be much more significant for a heterogeneous
interaction strength than for spatial roughness. This difference also applies
to the localization transition, where the polymer size becomes independent of
the chain length. The localization criterion can be quantified, depending on an
effective interaction strength and the length of the polymer chain.Comment: accepted in EPJB (the Journal formerly known as Journal de Physique
Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?
We assess the relationship between model size and complexity in the
time-varying parameter VAR framework via thorough predictive exercises for the
Euro Area, the United Kingdom and the United States. It turns out that
sophisticated dynamics through drifting coefficients are important in small
data sets while simpler models tend to perform better in sizeable data sets. To
combine best of both worlds, novel shrinkage priors help to mitigate the curse
of dimensionality, resulting in competitive forecasts for all scenarios
considered. Furthermore, we discuss dynamic model selection to improve upon the
best performing individual model for each point in time
Introducing shrinkage in heavy-tailed state space models to predict equity excess returns
We forecast S&P 500 excess returns using a flexible Bayesian econometric
state space model with non-Gaussian features at several levels. More precisely,
we control for overparameterization via novel global-local shrinkage priors on
the state innovation variances as well as the time-invariant part of the state
space model. The shrinkage priors are complemented by heavy tailed state
innovations that cater for potential large breaks in the latent states.
Moreover, we allow for leptokurtic stochastic volatility in the observation
equation. The empirical findings indicate that several variants of the proposed
approach outperform typical competitors frequently used in the literature, both
in terms of point and density forecasts
Unsupervised identification of topological order using predictive models
Machine-learning driven models have proven to be powerful tools for the
identification of phases of matter. In particular, unsupervised methods hold
the promise to help discover new phases of matter without the need for any
prior theoretical knowledge. While for phases characterized by a broken
symmetry, the use of unsupervised methods has proven to be successful,
topological phases without a local order parameter seem to be much harder to
identify without supervision. Here, we use an unsupervised approach to identify
topological phases and transitions out of them. We train artificial neural nets
to relate configurational data or measurement outcomes to quantities like
temperature or tuning parameters in the Hamiltonian. The accuracy of these
predictive models can then serve as an indicator for phase transitions. We
successfully illustrate this approach on both the classical Ising gauge theory
as well as on the quantum ground state of a generalized toric code.Comment: 12 pages, 13 figure
Hydrodynamics of steady state phloem transport with radial leakage of solute
Long-distance phloem transport occurs under a pressure gradient generated by the osmotic exchange of water associated with solute exchange in source and sink regions. But these exchanges also occur along the pathway, and yet their physiological role has almost been ignored in mathematical models of phloem transport. Here we present a steady state model for transport phloem which allows solute leakage, based on the Navier-Stokes and convection-diffusion equations which describe fluid motion rigorously. Sieve tube membrane permeability P s for passive solute exchange (and correspondingly, membrane reflection coefficient) influenced model results strongly, and had to lie in the bottom range of the values reported for plant cells for the results to be realistic. This smaller permeability reflects the efficient specialization of sieve tube elements, minimizing any diffusive solute loss favored by the large concentration difference across the sieve tube membrane. We also found there can be a specific reflection coefficient for which pressure profiles and sap velocities can both be similar to those predicted by the Hagen-Poiseuille equation for a completely impermeable tube.This study was supported by a Ph.D. grant from the
Helmholtz-DAAD (Deutscher Akademischer Austauschdienst)
Fellowship Programme Biogeosystems: Dynamics, Adaptation
and Adjustment
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