An Estimated New-Keynesian Model with Unemployment as Excess Supply of Labor

Wage stickiness is incorporated to a New-Keynesian model with variable capital to drive endogenous unemployment uctuations de ned as the log di¤erence between aggregate labor supply and aggregate labor demand. We estimated such model using Bayesian econometric techniques and quarterly U.S. data. The second-moment statistics of the unemployment rate in the model give a good t to those observed in U.S. data. Our results also show that wage-push shocks, demand shifts and monetary policy shocks are the three major determinants of unemployment fl uctuations. Compared to an estimated New-Keynesian model without unemployment (Smets and Wouters, 2007): wage stickiness is higher, labor supply elasticity is lower, the slope of the New-Keynesian Phillips curve is flatter, and the importance of technology innovations on output variability increases.

Emergent synchronisation properties of a refrigerator demand side management system

In order to analyse the possibilities of improving grid stability on island systems by local demand response mechanisms,a multi-agent simulation model is presented. To support the primary reserve, an under-frequency load shedding (UFLS)using refrigerator loads is modelled. The model represents the system at multiple scales, by recreating each refrigerator individually, and coupling the whole population of refrigerators to a model which simulates the frequency response of the energy system, allowing for cross-scale interactions. Using a simple UFLS strategy, emergent phenomena appear in the simulation. Synchronisation e ects among the individual loads were discovered, which can have strong, undesirable impacts on the system such as oscillations of loads and frequency. The phase transition from a stable to an oscillating system is discussed.

On demand translation for querying incompletely aligned datasets

More and more users aim at taking advantage of the existing Linked Open Data environment to formulate a query over a dataset and to then try to process the same query over different datasets, one after another, in order to obtain a broader set of answers. However, the heterogeneity of vocabularies used in the datasets on the one side, and the fact that the number of alignments among those datasets is scarce on the other, makes that querying task difficult for them. Considering this scenario we present in this paper a proposal that allows on demand translations of queries formulated over an original dataset, into queries expressed using the vocabulary of a targeted dataset. Our approach relieves users from knowing the vocabulary used in the targeted datasets and even more it considers situations where alignments do not exist or they are not suitable for the formulated query. Therefore, in order to favour the possibility of getting answers, sometimes there is no guarantee of obtaining a semantically equivalent translation. The core component of our proposal is a query rewriting model that considers a set of transformation rules devised from a pragmatic point of view. The feasibility of our scheme has been validated with queries defined in well known benchmarks and SPARQL endpoint logs, as the obtained results confirm.