7,688 research outputs found

    Capital Controls in Chile: Effective? Efficient?

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    New empirical evidence with regards to the effectiveness and efficiency of Chile's capital controls is provided here, based on more and better data on the range of controls and a broad assessment of their costs and benefits. The paper concludes that capital controls have been partially effective by raising the wedge between domestic and foreign interest rates, reducing aggregate net capital inflows, and changing the debt composition toward longer maturities, without significantly altering the real exchange rate. Part of these effects is temporary as the effectiveness of controls is eroded over time for a given interest rates differential. Controls may have been crucial by contributing to Chile's lower exposure to short-term foreign liabilities at the time of the 1997-98 international financial turmoil. However, achieving temporary macroeconomic benefits by relying on capital controls involves incurring in financial and growth effects that raise concerns about their efficiency. The costs that resulted from the policy mix that comprised the capital controls, in terms of quasi-fiscal losses and lower investment and growth, were probably not negligible.

    General Equilibrium Dynamics of External Shocks and Policy Changes in Chile

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    This paper develops a macroeconomic general-equilibrium model fully parameterized for the Chilean economy. The model’s basic relations are derived from intertemporal optimization by a group of rational forward-looking agents. The model also adds critical real-world features – such as short-run wage rigidities and a group of myopic agents – that generate deviations from the frictionless fullemployment equilibrium of the unconstrained neoclassical paradigm. The model is numerically simulated to illustrate the dynamics of Chile’s economy in response to the external shocks and policy shifts that led to the 1998-99 recession.

    Investigating Diversity in the Banking Sector in Europe: The Performance and Role of Savings Banks. CEPS Paperbacks. June 2009

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    In the aftermath of the financial crisis, the foundations of modern and innovative financial systems developed over decades have suffered serious damage. This has triggered massive state interventions and has led authorities to revamp the regulatory structures and frameworks. While many voices have called for a return to more traditional approaches to banking and finance, no one has argued the merits of diversity. This book investigates the merits of a diverse banking system with a special focus on the performance and role of savings banks in selected European countries where they are still prominent (Austria, Germany and Spain) and where they have progressively disappeared (Belgium and Italy). The theoretical and empirical arguments that are developed in this book tend to support the view that it is economically and socially beneficial to have ‘dual bottom-line’ institutions, such as savings banks. For those who accept this premise, it would suggest that policy-makers should not take or support actions that could jeopardise this valuable element of the financial system in various countries in Europe and of the emerging integrated European financial system

    Clostridium perfringens epsilon toxin induces blood brain barrier permeability via caveolae-dependent transcytosis and requires expression of MAL.

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    Clostridium perfringens epsilon toxin (ETX) is responsible for causing the economically devastating disease, enterotoxaemia, in livestock. It is well accepted that ETX causes blood brain barrier (BBB) permeability, however the mechanisms involved in this process are not well understood. Using in vivo and in vitro methods, we determined that ETX causes BBB permeability in mice by increasing caveolae-dependent transcytosis in brain endothelial cells. When mice are intravenously injected with ETX, robust ETX binding is observed in the microvasculature of the central nervous system (CNS) with limited to no binding observed in the vasculature of peripheral organs, indicating that ETX specifically targets CNS endothelial cells. ETX binding to CNS microvasculature is dependent on MAL expression, as ETX binding to CNS microvasculature of MAL-deficient mice was not detected. ETX treatment also induces extravasation of molecular tracers including 376Da fluorescein salt, 60kDA serum albumin, 70kDa dextran, and 155kDA IgG. Importantly, ETX-induced BBB permeability requires expression of both MAL and caveolin-1, as mice deficient in MAL or caveolin-1 did not exhibit ETX-induced BBB permeability. Examination of primary murine brain endothelial cells revealed an increase in caveolae in ETX-treated cells, resulting in dynamin and lipid raft-dependent vacuolation without cell death. ETX-treatment also results in a rapid loss of EEA1 positive early endosomes and accumulation of large, RAB7-positive late endosomes and multivesicular bodies. Based on these results, we hypothesize that ETX binds to MAL on the apical surface of brain endothelial cells, causing recruitment of caveolin-1, triggering caveolae formation and internalization. Internalized caveolae fuse with early endosomes which traffic to late endosomes and multivesicular bodies. We believe that these multivesicular bodies fuse basally, releasing their contents into the brain parenchyma

    Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning

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    Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests. The control of these systems is typically formulated as a large network optimization problem, and reinforcement learning (RL) has recently emerged as a promising approach to solve the open challenges in this space. Recent centralized RL approaches focus on learning from online data, ignoring the per-sample-cost of interactions within real-world transportation systems. To address these limitations, we propose to formalize the control of AMoD systems through the lens of offline reinforcement learning and learn effective control strategies using solely offline data, which is readily available to current mobility operators. We further investigate design decisions and provide empirical evidence based on data from real-world mobility systems showing how offline learning allows to recover AMoD control policies that (i) exhibit performance on par with online methods, (ii) allow for sample-efficient online fine-tuning and (iii) eliminate the need for complex simulation environments. Crucially, this paper demonstrates that offline RL is a promising paradigm for the application of RL-based solutions within economically-critical systems, such as mobility systems

    Optimization of BEM-Based Cooling Channels Injection Moulding Using Model Reduction

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    Issu de : ESAFORM 2008 - 11th International ESAFORM conference on material forming, Lyon, FRANCE, April 23-25 2008International audienceToday, around 30% of manufactured plastic goods rely on injection moulding. The cooling time can represent more than 70% of the injection cycle. In this process, heat transfer during the cooling step has a great influence both on the quality of the final parts that are produced, and on the moulding cycle time. Models based on a full 3D finite element method renders unpractical the use of optimization of the design and placement of the cooling channel in injection moulds. We have extended the use of boundary element method (BEM) to this process. We introduce in this paper a practical methodology to optimize both the position and the shape of the cooling channels in injection moulding processes. We couple the direct computation with an optimization algorithm such as SQP (Sequential Quadratic Programming). First, we propose an implementation of the model reduction in the BEM solver. This technique permits to reduce considerably the computing time during the linear system resolution (unsteady case). Secondly, we couple it with an optimization algorithm to evaluate its potentiality. For example, we can minimize the maximal temperature on the cavity surface subject to a temperature uniformity constraint. Thirdly, we present encouraging computational results on plastic parts that show that our optimization methodology is viable
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