Long memory and nonlinearities in realized volatility: a Markov switching approach.

Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and predictive densities. The insample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample results at several forecast horizons, show that introducing these nonlinearities produces superior forecasts over those obtained from nested models.

Subnational Government Bailouts in OECD Countries: Four Case Studies

We present four case studies of bailouts of subnational governments in Australia, Germany, Italy and Sweden. The case studies show that bailouts can occur in a diverse set of institutions shaping the relations between central and subnational governments. Surpisingly, there is little evidence in favor of the `too big to fail` argument explaining bailouts. In contrast, elements of political favoritism play some role in most cases. The cases also indicate the importance of properly designing principal-agent relationships in the decentralization of public finances. Constitutional mandates for uniform provision of public services and attempts by the central government to dominate subnational governments in matters of fiscal policy seem to be conducive to bailouts.

Social learning under inferential attacks

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries are unaware of the true hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have minimal or no information about the network model

Network classifiers based on social learning

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing so, the proposed architecture is able to improve prediction performance over time with unlabeled data. Inspired by social learning algorithms, which require prior knowledge of the observations distribution, we propose a Social Machine Learning (SML) paradigm that is able to exploit the imperfect models generated during the learning phase. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers. Simulations with an ensemble of feedforward neural networks are provided to illustrate the theoretical results

Visualizing cellular heterogeneity by quantifying the dynamics of MAPK activity in live mammalian cells with synthetic fluorescent biosensors.

Mitogen-Activated Protein Kinases (MAPKs) control a wide array of cellular functions by transducing extracellular information into defined biological responses. In order to understand how these pathways are regulated, dynamic single cell measurements are highly needed. Fluorescence microscopy is well suited to perform these measurements. However, more dynamic and sensitive biosensors that allow the quantification of signaling activity in living mammalian cells are required. We have engineered a synthetic fluorescent substrate for human MAPKs (ERK, JNK and p38) that relocates from the nucleus to the cytoplasm when phosphorylated by the kinases. We demonstrate that this reporter displays an improved response compared to other relocation biosensors. This assay allows to monitor the heterogeneity in the MAPK response in a population of isogenic cells, revealing pulses of ERK activity upon a physiological EGFR stimulation. We show applicability of this approach to the analysis of multiple cancer cell lines and primary cells as well as its application in vivo to developing tumors. Using this ERK biosensor, dynamic single cell measurements with high temporal resolution can be obtained. These MAPK reporters can be widely applied to the analysis of molecular mechanisms of MAPK signaling in healthy and diseased state, in cell culture assays or in vivo

Assessment of the fiscal stance appropriate for the euro area in 2019

On 18 June 2018, the European Fiscal Board (EFB) has published its assessment of the general orientation of fiscal policy in the euro area. The report concludes that the favourable economic outlook offers a prime opportunity to rebuild fiscal buffers. Especially euro area Member States with a high government debt-to-GDP ratio need to do more than simply accrue the budgetary benefits of the economic expansion. Lest we repeat the mistakes of the past and rob ourselves of room to manoeuvre when the next crisis hits, this is the time to move towards a somewhat restrictive orientation of fiscal policy in the euro area. It is also the time to upgrade the EU's fiscal framework and prepare a capacity for joint stabilisation for the euro area