A New Method For Combining Detrending Techniques with Application to Business Cycle Synchronization of the New EU Members

Decomposing output into trend and cyclical components is an uncertain exercise and depends on the method applied. It is an especially dubious task for countries undergoing large structural changes, such as transition countries. Despite their deficiencies, however, univariate detrending methods are frequently adopted for both policy oriented and academic research. This paper proposes a new procedure for combining univariate detrending techniques which is based on revisions of the estimated output gaps adjusted by the variance of and the correlation among output gaps. The procedure is applied to the study of the similarity of business cycles between the euro area and new EU Member States.

Fiscal Divergence and Business Cycle Synchronization: Irresponsibility is Idiosyncratic

Using a panel of 21 OECD countries and 40 years of annual data, we find that countries with similar government budget positions tend to have business cycles that fluctuate more closely. That is, fiscal convergence (in the form of persistently similar ratios of government surplus/deficit to GDP) is systematically associated with more synchronized business cycles. We also find evidence that reduced fiscal deficits increase business cycle synchronization. The Maastricht "convergence criteria," used to determine eligibility for EMU, encouraged fiscal convergence and deficit reduction. They may thus have indirectly moved Europe closer to an optimum currency area, by reducing countries’ abilities to create idiosyncratic fiscal shocks. Our empirical results are economically and statistically significant, and robust.

A fiskális divergencia és a konjunktúraciklusok együttmozgása - a felelőtlen fiskális politika az aszimmetrikus sokkok forrása (Fiscal divergence and business cycle synchronization: irresponsibility is idiosyncratic)

A világ 115 országának - köztük 21 OECD-tagország - 40 évnyi adatait vizsgálva, arra a következtetésre jutottunk, hogy a hasonló állami költségvetési pozíciójú országok konjunktúraciklusai között szorosabb együttmozgás mutatható ki. Azaz, a fiskális konvergenciát (amelyet a költségvetési egyenleg GDP-hez viszonyt arányának konvergenciájaként definiáltunk) összehangoltabb konjunktúraciklusokkal lehet összefüggésbe hozni. Kutatásaink során arra is találtunk bizonyítékot, hogy a kisebb mértékű költségvetési deficitek növelik a konjunktúraciklusok együttmozgását. A maastrichti konvergenciakritériumok - amelyek az európai monetáris unió követelményeinek való megfelelést hivatottak meghatározni - a fiskális konvergenciát és a költségvetési deficit csökkentését ösztönözték, s ezzel közvetett módon hozzásegítették Európát egy optimális valutaövezet létrehozásához azáltal, hogy csökkent az egyes országok lehetősége a felelőtlen fiskális politika által gerjesztett sokkhatások létrehozására. Az általunk feltárt empirikus eredmények gazdasági és statisztikai szempontból is szignifikánsak és robusztusak. _____ Using panels of 115 countries of world – including 21 OECD countries – and 40 years of annual data, the authors find that countries with similar government budget positions tend to have business cycles that fluctuate more closely. Thus fiscal convergence (in the form of persistently similar ratios of government surplus/deficit to GDP) is systemati-cally associated with more strongly synchronized business cycles. Evidence is also found that reduced fiscal deficits increase business-cycle synchronization. The Maastricht "con-vergence criteria", used to determine eligibility for EMU, encouraged fiscal convergence and deficit reduction. So they may, indirectly, have moved Europe closer to an optimum currency area, by reducing countries abilities to create idiosyncratic fiscal shocks. The empirical results of the study are economically and statistically significant, and robust.

Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^

Parameter space of experimental chaotic circuits with high-precision control parameters

The authors thank Professor Iber^e Luiz Caldas for the suggestions and encouragement. The authors F.F.G.d.S., R.M.R., J.C.S., and H.A.A. acknowledge the Brazilian agency CNPq and state agencies FAPEMIG, FAPESP, and FAPESC, and M.S.B. also acknowledges the EPSRC Grant Ref. No. EP/I032606/1.

A Chaotic Spread Spectrum System for Underwater Acoustic Communication

The work is supported in part by NSFC (Grant no. 61172070), IRT of Shaanxi Province (2013KCT-04), EPSRC (Grant no.Ep/1032606/1).

Survivability of Deterministic Dynamical Systems

Acknowledgements We acknowledge gratefully the support of BMBF, CoNDyNet, FK. 03SF0472A, of the EIT Climate-KIC project SWIPO and Nora Molkenthin for illustrating our illustration of the concept of survivability using penguins. We thank Martin Rohden for providing us with the UK high-voltage transmission grid topology and Yang Tang for very useful discussions. The publication of this article was funded by the Open Access Fund of the Leibniz Association.

All-analogue real-time filter bank OFDM over 50 Km of SSMF using a novel synchronization technique

A 21.6 Gbit/s 1.78 bit/s/Hz OFDM signal is transmitted over 50 Km of fiber without using DSP in the transmitter or the receiver. The synchronization scheme only requires one PLL to synchronize all the subcarriers.

Collective excitability, synchronization, and array-enhanced coherence resonance in a population of lasers with a saturable absorber

In this article we present a numerical study of the collective dynamics in a population of coupled semiconductor lasers with a saturable absorber, operating in the excitable regime under the action of additive noise. We demonstrate that temporal and intensity synchronization takes place in a broad region of the parameter space and for various array sizes. The synchronization is robust and occurs even for a set of nonidentical coupled lasers. The cooperative nature of the system results in a self-organization process which enhances the coherence of the single element of the population too and can have broad impact for detection purposes, for building all-optical simulators of neural networks and in the field of photonics-based computation.

Conditional moment closure for chemical reactions in laminar chaotic flows

We implement conditional moment closure (CMC) for simulation of chemical reactions in laminar chaotic flows. The CMC approach predicts the expected concentration of reactive species, conditional upon the concentration of a corresponding nonreactive scalar. Closure is obtained by neglecting the difference between the local concentration of the reactive scalar and its conditional average. We first use a Monte Carlo method to calculate the evolution of the moments of a conserved scalar; we then reconstruct the corresponding probability density function and dissipation rate. Finally, the concentrations of the reactive scalars are determined. The results are compared (and show excellent agreement) with full numerical simulations of the reaction processes in a chaotic laminar flow. This is a preprint of an article published in AlChE Journal copyright (2007) American Institute of Chemical Engineers: http://www3.interscience.wiley.com/

Chaotic mixing of a competitive-consecutive reaction

The evolution of a competitive-consecutive chemical reaction is computed numerically in a two-dimensional chaotic fluid flow with initially segregated reactants. Results from numerical simulations are used to evaluate a variety of reduced models commonly adopted to model the full advection-reaction-diffusion problem. Particular emphasis is placed upon fast reactions, where the yield varies most significantly with Peclet number (the ratio of diffusive to advective time scales). When effects of the fluid mechanical mixing are strongest, we find that the yield of the reaction is underestimated by a one-dimensional lamellar model that ignores the effects of fluid mixing, but overestimated by two other lamellar models that include fluid mixing.

Reduced models of chemical reaction in chaotic flows

We describe and evaluate two reduced models for nonlinear chemical reactions in a chaotic laminar flow. Each model involves two separate steps to compute the chemical composition at a given location and time. The “manifold tracking model” first tracks backwards in time a segment of the stable manifold of the requisite point. This then provides a sample of the initial conditions appropriate for the second step, which requires solving one-dimensional problems for the reaction in Lagrangian coordinates. By contrast, the first step of the “branching trajectories model” simulates both the advection and diffusion of fluid particles that terminate at the appropriate point; the chemical reaction equations are then solved along each of the branched trajectories in a second step. Results from each model are compared with full numerical simulations of the reaction processes in a chaotic laminar flow.

Synchronization of Heteroclinic Circuits Through Learning in Chains of Neural Motifs

The synchronization of oscillatory activity in networks of neural networks is usually implemented through coupling the state variables describing neuronal dynamics. In this study we discuss another but complementary mechanism based on a learning process with memory. A driver network motif, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven motif, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner motif can dynamically copy the coupling pattern of the teacher and thus synchronize oscillations with the teacher. Then, we demonstrate that the replication of the WLC dynamics occurs for intermediate memory lengths only. In a unidirectional chain of N motifs coupled through teacher-learner paradigm the time interval required for pattern replication grows linearly with the chain size, hence the learning process does not blow up and at the end we observe phase synchronized oscillations along the chain. We also show that in a learning chain closed into a ring the network motifs come to a consensus, i.e. to a state with the same connectivity pattern corresponding to the mean initial pattern averaged over all network motifs.