Toward a Statistically Robust Assessment of Social and Solidarity Economy Actors. Conceptual Development and Empirical Validation

Many definitions and debates exist about the core characteristics of social and solidarity economy (SSE) and its actors. Among others, legal forms, profit, geographical scope, and size as criteria for identifying SSE actors often reveal dissents among SSE scholars. Instead of using a dichotomous, either-in-or-out definition of SSE actors, this paper presents an assessment tool that takes into account multiple dimensions to offer a more comprehensive and nuanced view of the field. We first define the core dimensions of the assessment tool by synthesizing the multiple indicators found in the literature. We then empirically test these dimensions and their interrelatedness and seek to identify potential clusters of actors. Finally we discuss the practical implications of our model.

Discrete devaluations and multiple equilibria in a first generation model of currency crises

The first generation models of currency crises have often been criticized because they predict that, in the absence of very large triggering shocks, currency attacks should be predictable and lead to small devaluations. This paper shows that these features of first generation models are not robust to the inclusion of private information. In particular, this paper analyzes a generalization of the Krugman-Flood-Garber (KFG) model, which relaxes the assumption that all consumers are perfectly informed about the level of fundamentals. In this environment, the KFG equilibrium of zero devaluation is only one of many possible equilibria. In all the other equilibria, the lack of perfect information delays the attack on the currency past the point at which the shadow exchange rate equals the peg, giving rise to unpredictable and discrete devaluations.

Bilateral dorsal and ventral fiber pathways for the processing of affective prosody identified by probabilistic fiber tracking.

Dorsal and ventral pathways for syntacto-semantic speech processing in the left hemisphere are represented in the dual-stream model of auditory processing. Here we report new findings for the right dorsal and ventral temporo-frontal pathway during processing of affectively intonated speech (i.e. affective prosody) in humans, together with several left hemispheric structural connections, partly resembling those for syntacto-semantic speech processing. We investigated white matter fiber connectivity between regions responding to affective prosody in several subregions of the bilateral superior temporal cortex (secondary and higher-level auditory cortex) and of the inferior frontal cortex (anterior and posterior inferior frontal gyrus). The fiber connectivity was investigated by using probabilistic diffusion tensor based tractography. The results underscore several so far underestimated auditory pathway connections, especially for the processing of affective prosody, such as a right ventral auditory pathway. The results also suggest the existence of a dual-stream processing in the right hemisphere, and a general predominance of the dorsal pathways in both hemispheres underlying the neural processing of affective prosody in an extended temporo-frontal network.

Crises and growth: A re-evaluation

We address the question of whether growth and welfare can be higher in crisis prone economies. First, we show that there is a robust empirical link between per-capita GDP growth and negative skewness of credit growth across countries with active financial markets. That is, countries that have experienced occasional crises have grown on average faster than countries with smooth credit conditions. We then present a two-sector endogenous growth model in which financial crises can occur, and analyze the relationship between financial fragility and growth. The underlying credit market imperfections generateborrowing constraints, bottlenecks and low growth. We show that under certain conditions endogenous real exchange rate risk arises and firms find it optimal to take on credit risk in the form of currency mismatch. Along such a risky path average growth is higher, but self-fulfilling crises occur occasionally. Furthermore, we establish conditions under which the adoption of credit risk is welfare improving and brings the allocation nearer to the Pareto optimal level. The design of the model is motivated by several features of recent crises: credit risk in the form of foreign currency denominated debt; costly crises that generate firesales and widespread bankruptcies; and asymmetric sectorial responses, wherethe nontradables sector falls more than the tradables sector in the wake of crises.

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paperexplores the consistency of some of its standard techniques. We focus in particular on prevailingwholesale electricity trading simulation methods. We include different supply and demandrepresentations and propose the Experience-Weighted Attractions method to include severalbehavioural algorithms. We compare the results across assumptions and to economic theorypredictions. The match is good under best-response and reinforcement learning but not underfictitious play. The simulations perform well under flat and upward-slopping supply bidding,and also for plausible demand elasticity assumptions. Learning is influenced by the number ofbids per plant and the initial conditions. The overall conclusion is that agent-based simulationassumptions are far from innocuous. We link their performance to underlying features, andidentify those that are better suited to model wholesale electricity markets.

Shocks and institutions in a job matching model

This paper explains the divergent behavior of European an US unemploymentrates using a job market matching model of the labor market with aninteraction between shocks an institutions. It shows that a reduction inTF growth rates, an increase in real interest rates, and an increase intax rates leads to a permanent increase in unemployment rates when thereplacement rates or initial tax rates are high, while no increase inunemployment occurs when institutions are "employment friendly". The paperalso shows that an increase in turbulence, modelle as an increase probabilityof skill loss, is not a robust explanation for the European unemploymentpuzzle in the context of a matching model with both endogenous job creationand job estruction.

Keeping up with the Joneses: An international asset pricing model

We derive an international asset pricing model that assumes local investorshave preferences of the type "keeping up with the Joneses." In aninternational setting investors compare their current wealth with that oftheir peers who live in the same country. In the process of inferring thecountry's average wealth, investors incorporate information from the domesticmarket portfolio. In equilibrium, this gives rise to a multifactor CAPMwhere, together with the world market price of risk, there existscountry-speciffic prices of risk associated with deviations from thecountry's average wealth level. The model performs signifficantly better, interms of explaining cross-section of returns, than the international CAPM.Moreover, the results are robust, both for conditional and unconditionaltests, to the inclusion of currency risk, macroeconomic sources of risk andthe Fama and French HML factor.

iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach.

The n-octanol/water partition coefficient (log Po/w) is a key physicochemical parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log Po/w on a cleansed data set of more than 17,500 molecules. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of molecules. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The physical basis of our original GB/SA approach together with its predictive capacity, computational efficiency (1 to 2 s per molecule), and tridimensional molecular graphics capability lay the foundations for a promising predictor, the implicit log P method (iLOGP), to complement the portfolio of drug design tools developed and provided by the SIB Swiss Institute of Bioinformatics.

Comment to "Weak instruments robust tests in GMM and the New Keynesian Phillips curve" by Frank Kleibergen and Sophocles Mavroeidis

I discuss the identifiability of a structural New Keynesian Phillips curve when it is embedded in a small scale dynamic stochastic general equilibrium model. Identification problems emerge because not all the structural parameters are recoverable from the semi-structural ones and because the objective functions I consider are poorly behaved. The solution and the moment mappings are responsible for the problems.

Atrial septal pacing for the termination of atrial fibrillation: study in a biophysical model of human atria.

AIMS: While successful termination by pacing of organized atrial tachycardias has been observed in patients, single site rapid pacing has not yet led to conclusive results for the termination of atrial fibrillation (AF). The purpose of this study was to evaluate a novel atrial septal pacing algorithm for the termination of AF in a biophysical model of the human atria. METHODS AND RESULTS: Sustained AF was generated in a model based on human magnetic resonance images and membrane kinetics. Rapid pacing was applied from the septal area following a dual-stage scheme: (i) rapid pacing for 10-30 s at pacing intervals 62-70% of AF cycle length (AFCL), (ii) slow pacing for 1.5 s at 180% AFCL, initiated by a single stimulus at 130% AFCL. Atrial fibrillation termination success rates were computed. A mean success rate for AF termination of 10.2% was obtained for rapid septal pacing only. The addition of the slow pacing phase increased this rate to 20.2%. At an optimal pacing cycle length (64% AFCL) up to 29% of AF termination was observed. CONCLUSION: The proposed septal pacing algorithm could suppress AF reentries in a more robust way than classical single site rapid pacing. Experimental studies are now needed to determine whether similar termination mechanisms and rates can be observed in animals or humans, and in which types of AF this pacing strategy might be most effective.

Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.

Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 µGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed.Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure.

MSISpIC: A Probabilistic Scan Matching Algorithm Using a Mechanical Scanned Imaging Sonar

This paper compares two well known scan matching algorithms: the MbICP and the pIC. As a result of the study, it is proposed the MSISpIC, a probabilistic scan matching algorithm for the localization of an Autonomous Underwater Vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), and the robot displacement estimated through dead-reckoning with the help of a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method is an extension of the pIC algorithm. Its major contribution consists in: 1) using an EKF to estimate the local path traveled by the robot while grabbing the scan as well as its uncertainty and 2) proposing a method to group into a unique scan, with a convenient uncertainty model, all the data grabbed along the path described by the robot. The algorithm has been tested on an AUV guided along a 600m path within a marina environment with satisfactory results

Genetic manipulation of adult-born hippocampal neurons rescues memory in a mouse model of Alzheimer's disease.

In adult mammals, neural progenitors located in the dentate gyrus retain their ability to generate neurons and glia throughout lifetime. In rodents, increased production of new granule neurons is associated with improved memory capacities, while decreased hippocampal neurogenesis results in impaired memory performance in several memory tasks. In mouse models of Alzheimer's disease, neurogenesis is impaired and the granule neurons that are generated fail to integrate existing networks. Thus, enhancing neurogenesis should improve functional plasticity in the hippocampus and restore cognitive deficits in these mice. Here, we performed a screen of transcription factors that could potentially enhance adult hippocampal neurogenesis. We identified Neurod1 as a robust neuronal determinant with the capability to direct hippocampal progenitors towards an exclusive granule neuron fate. Importantly, Neurod1 also accelerated neuronal maturation and functional integration of new neurons during the period of their maturation when they contribute to memory processes. When tested in an APPxPS1 mouse model of Alzheimer's disease, directed expression of Neurod1 in cycling hippocampal progenitors conspicuously reduced dendritic spine density deficits on new hippocampal neurons, to the same level as that observed in healthy age-matched control animals. Remarkably, this population of highly connected new neurons was sufficient to restore spatial memory in these diseased mice. Collectively our findings demonstrate that endogenous neural stem cells of the diseased brain can be manipulated to become new neurons that could allow cognitive improvement.

Determinants of Corporate Disclosure : A Multicountry, Multilevel Model

Abstract The complexity of the current business world is making corporate disclosure more and more important for information users. These users, including investors, financial analysts, and government authorities rely on the disclosed information to make their investment decisions, analyze and recommend shares, and to draft regulation policies. Moreover, the globalization of capital markets has raised difficulties for information users in understanding the differences incorporate disclosure across countries and across firms. Using a sample of 797 firms from 34 countries, this thesis advances the literature on disclosure by illustrating comprehensively the disclosure determinants originating at firm systems and national systems based on the multilevel latent variable approach. Under this approach, the overall variation associated with the firm-specific variables is decomposed into two parts, the within-country and the between-country part. Accordingly, the model estimates the latent association between corporate disclosure and information demand at two levels, the within-country and the between-country level. The results indicate that the variables originating from corporate systems are hierarchically correlated with those from the country environment. The information demand factor indicated by the number of exchanges listed and the number of analyst recommendations can significantly explain the variation of corporate disclosure for both "within" and "between" countries. The exogenous influences of firm fundamentals-firm size and performance-are exerted indirectly through the information demand factor. Specifically, if the between-country variation in firm variables is taken into account, only the variables of legal systems and economic growth keep significance in explaining the disclosure differences across countries. These findings strongly support the hypothesis that disclosure is a response to both corporate systems and national systems, but the influence of the latter on disclosure reflected significantly through that of the former. In addition, the results based on ADR (American Depositary Receipt) firms suggest that the globalization of capital markets is harmonizing the disclosure behavior of cross-boundary listed firms, but it cannot entirely eliminate the national features in disclosure and other firm-specific characteristics.

Modelling of weather radar echoes from anomalous propagation using a hybrid parabolic equation method and NWP model data

Contamination of weather radar echoes by anomalous propagation (anaprop) mechanisms remains a serious issue in quality control of radar precipitation estimates. Although significant progress has been made identifying clutter due to anaprop there is no unique method that solves the question of data reliability without removing genuine data. The work described here relates to the development of a software application that uses a numerical weather prediction (NWP) model to obtain the temperature, humidity and pressure fields to calculate the three dimensional structure of the atmospheric refractive index structure, from which a physically based prediction of the incidence of clutter can be made. This technique can be used in conjunction with existing methods for clutter removal by modifying parameters of detectors or filters according to the physical evidence for anomalous propagation conditions. The parabolic equation method (PEM) is a well established technique for solving the equations for beam propagation in a non-uniformly stratified atmosphere, but although intrinsically very efficient, is not sufficiently fast to be practicable for near real-time modelling of clutter over the entire area observed by a typical weather radar. We demonstrate a fast hybrid PEM technique that is capable of providing acceptable results in conjunction with a high-resolution terrain elevation model, using a standard desktop personal computer. We discuss the performance of the method and approaches for the improvement of the model profiles in the lowest levels of the troposphere.