11,981 research outputs found
Extinction transitions in correlated external noise
We analyze the influence of long-range correlated (colored) external noise on
extinction phase transitions in growth and spreading processes. Uncorrelated
environmental noise (i.e., temporal disorder) was recently shown to give rise
to an unusual infinite-noise critical point [Europhys. Lett. 112, 30002
(2015)]. It is characterized by enormous density fluctuations that increase
without limit at criticality. As a result, a typical population decays much
faster than the ensemble average which is dominated by rare events. Using the
logistic evolution equation as an example, we show here that positively
correlated (red) environmental noise further enhances these effects. This
means, the correlations accelerate the decay of a typical population but slow
down the decay of the ensemble average. Moreover, the mean time to extinction
of a population in the active, surviving phase grows slower than a power law
with population size. To determine the complete critical behavior of the
extinction transition, we establish a relation to fractional random walks, and
we perform extensive Monte-Carlo simulations.Comment: 11 pages, 12 figures, Final versio
Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases
This paper formalizes the process of updating the nowcast and forecast on out-put and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing "news" on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real variables and this effect is larger than that of the Employment Report. When we control for timeliness of the releases, the effect of hard data becomes sizeable. Prices and quantities affect the precision of the estimates of inflation while GDP is only affected by real variables and interest rates
Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases
This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing "news" on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real variables and this effect is larger than that of the Employment Report. When we control for timeliness of the releases, the effect of hard data becomes sizeable. Prices and quantities affect the precision of the estimates of inflation while GDP is only affected by real variables and interest rates. JEL Classification: E52, C33, C53factor model, forecasting, Large Data Sets, monetary policy, news, Real Time Data
Detecting periodicity in experimental data using linear modeling techniques
Fourier spectral estimates and, to a lesser extent, the autocorrelation
function are the primary tools to detect periodicities in experimental data in
the physical and biological sciences. We propose a new method which is more
reliable than traditional techniques, and is able to make clear identification
of periodic behavior when traditional techniques do not. This technique is
based on an information theoretic reduction of linear (autoregressive) models
so that only the essential features of an autoregressive model are retained.
These models we call reduced autoregressive models (RARM). The essential
features of reduced autoregressive models include any periodicity present in
the data. We provide theoretical and numerical evidence from both experimental
and artificial data, to demonstrate that this technique will reliably detect
periodicities if and only if they are present in the data. There are strong
information theoretic arguments to support the statement that RARM detects
periodicities if they are present. Surrogate data techniques are used to ensure
the converse. Furthermore, our calculations demonstrate that RARM is more
robust, more accurate, and more sensitive, than traditional spectral
techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified
styl
Optimal phase space projection for noise reduction
In this communication we will re-examine the widely studied technique of
phase space projection. By imposing a time domain constraint (TDC) on the
residual noise, we deduce a more general version of the optimal projector,
which includes those appearing in previous literature as subcases but does not
assume the independence between the clean signal and the noise. As an
application, we will apply this technique for noise reduction. Numerical
results show that our algorithm has succeeded in augmenting the signal-to-noise
ratio (SNR) for simulated data from the R\"ossler system and experimental
speech record.Comment: Accepted version for PR
Compensable Injury in Back Claims
This article is the product of many years uncertainty and resulting unhappiness in advising clients, compensation insurers and their claims representatives-in their handling of back claims, and in the writer\u27s own preparation and trial of such proceedings.No doubt this situation has been intensified by the fact that North Carolina is in the very small minority of jurisdictions limiting compensability by requiring an accident as a condition precedent, and yet recognizing this handicap, seeking exceptions where it could, to the accomplishment of justice at the cost of confusion
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