Extracting the Italian output gap: a Bayesian approach

During the last decades particular effort has been directed towards understanding and predicting the relevant state of the business cycle with the objective of decomposing permanent shocks from those having only a transitory impact on real output. This trend--cycle decomposition has a relevant impact on several economic and fiscal variables and constitutes by itself an important indicator for policy purposes. This paper deals with trend--cycle decomposition for the Italian economy having some interesting peculiarities which makes it attractive to analyse from both a statistic and an historical perspective. We propose an univariate model for the quarterly real GDP, subsequently extended to include the price dynamics through a Phillips curve. This study considers a series of the Italian quarterly real GDP recently released by OECD which includes both the 1960s and the recent global financial crisis of 2007--2008. Parameters estimate as well as the signal extraction are performed within the Bayesian paradigm which effectively handles complex models where the parameters enter the log--likelihood function in a strongly nonlinear way. A new Adaptive Independent Metropolis--within--Gibbs sampler is then developed to efficiently simulate the parameters of the unobserved cycle. Our results suggest that inflation influences the Output Gap estimate, making the extracted Italian OG an important indicator of inflation pressures on the real side of the economy, as stated by the Phillips theory. Moreover, our estimate of the sequence of peaks and troughs of the Output Gap is in line with the OECD official dating of the Italian business cycle

Riconoscimento olfattivo dei rifugi terrestri nelle femmine di Salamandrina dagli occhiali settentrionale Salamandrina perspicillata (Caudata, Salamandridae)

Le percezioni olfattive sono ampiamente usate nel mondo animale per discriminare tra individui, gruppi, consanguinei e tra specie. La Salamandrina dagli occhiali settentrionale (Salamandrina perspicillata) è una specie semi terrestre e dal comportamento elusivo. Le femmine adulte si recano in acqua solo durante il periodo della deposizione delle uova mentre trascorrono la maggior parte della loro vita, così come fanno i maschi, in ambiente terrestre, rifugiandosi, per ridurre al minimo il rischio di disidratazione, in fessure delle rocce, sotto massi e tronchi. Utilizzando femmine riproduttive, abbiamo effettuato un esperimento per testare se le tracce chimiche lasciate sul substrato da ogni individuo e quelle lasciate da altre femmine avevano un ruolo nella scelta del rifugio terrestre. Per verificare questo abbiamo condotto una serie di test non forzati con possibilità di una doppia scelta del rifugio. In ogni test infatti ogni salamandrina poteva scegliere tra due rifugi artificiali (piccoli tubi in plastica) che differivano per le tracce chimiche in essi lasciate. I dati sono stati analizzati usando la distribuzione binomiale. I risultati indicano che l'olfatto guida le salamandrine nella scelta del rifugio. Infatti gli animali erano in grado di (i) discriminare tra i tubi marcati da loro stessi e quelli privi di odori (PChemical cues are used as ubiquitous markers of individual, group, kinship, and species identity. Northern Spectacled Salamander (Salamandrina perspicillata) is a semi-terrestrial and elusive species. Females can be found in water bodies just in the spawning season but spend most of their life, as well as males do, in terrestrial shelters such as cracks, crevices and under stones to reduce the risks of dehydration. We have investigated whether, in reproductive females, animal's own and conspecific chemical cues play a role in the shelter choice. We performed unforced "two-choice system" tests in order to study the behavioural response of salamanders to scent marks. For each test, the choice between two artificial shelters (plastic tubes) was offered to each focal individual. Data were analyzed using the binomial distribution. Our results show that Salamandrina use the sense of smell in the terrestrial shelter choice as animals (i) were capable to discriminate between a tube previously used by itself and a unused one (

Complex Grid Computing

This article investigates the performance of grid computing systems whose interconnections are given by random and scale-free complex network models. Regular networks, which are common in parallel computing architectures, are also used as a standard for comparison. The processing load is assigned to the processing nodes on demand, and the efficiency of the overall computing is quantified in terms of the respective speed-ups. It is found that random networks allow higher computing efficiency than their scale-free counterparts as a consequence of the smaller number of isolated clusters implied by the former model. At the same time, for fixed cluster sizes, the scale free model tend to provide slightly better efficiency. Two modifications of the random and scale free paradigms, where new connections tend to favor more recently added nodes, are proposed and shown to be more effective for grid computing than the standard models. A well-defined correlation is observed between the topological properties of the network and their respective computing efficiency.Comment: 5 pages, 2 figure

Bayesian nonparametric forecasting of monotonic functional time series

We propose a Bayesian nonparametric approach to modelling and predicting a class of functional time series with application to energy markets, based on fully observed, noise-free functional data. Traders in such contexts conceive profitable strategies if they can anticipate the impact of their bidding actions on the aggregate demand and supply curves, which in turn need to be predicted reliably. Here we propose a simple Bayesian nonparametric method for predicting such curves, which take the form of monotonic bounded step functions. We borrow ideas from population genetics by defining a class of interacting particle systems to model the functional trajectory, and develop an implementation strategy which uses ideas from Markov chain Monte Carlo and approximate Bayesian computation techniques and allows to circumvent the intractability of the likelihood. Our approach shows great adaptation to the degree of smoothness of the curves and the volatility of the functional series, proves to be robust to an increase of the forecast horizon and yields an uncertainty quantification for the functional forecasts. We illustrate the model and discuss its performance with simulated datasets and on real data relative to the Italian natural gas market.Comment: To appear on the Electronic Journal of Statistic

PFIs Involving Multiple Public Partners:A Case Study from the Italian Health Care Sector

The financial crisis experienced by many countries since 2008 has given new importance to private finance initiatives (PFIs) for providing public services. This paper analyses the relationships between multiple public and private sector actors participating in a PFI in the healthcare sector in order to better understand the motives and behaviour of public and private sector partners. High levels of trust and the active participation of a regulatory body were found to be significant factors in terms of creating a partnership that benefits all sides

Predictive inference with Fleming--Viot-driven dependent Dirichlet processes

We consider predictive inference using a class of temporally dependent Dirichlet processes driven by Fleming--Viot diffusions, which have a natural bearing in Bayesian nonparametrics and lend the resulting family of random probability measures to analytical posterior analysis. Formulating the implied statistical model as a hidden Markov model, we fully describe the predictive distribution induced by these Fleming--Viot-driven dependent Dirichlet processes, for a sequence of observations collected at a certain time given another set of draws collected at several previous times. This is identified as a mixture of P\'olya urns, whereby the observations can be values from the baseline distribution or copies of previous draws collected at the same time as in the usual P\`olya urn, or can be sampled from a random subset of the data collected at previous times. We characterise the time-dependent weights of the mixture which select such subsets and discuss the asymptotic regimes. We describe the induced partition by means of a Chinese restaurant process metaphor with a conveyor belt, whereby new customers who do not sit at an occupied table open a new table by picking a dish either from the baseline distribution or from a time-varying offer available on the conveyor belt. We lay out explicit algorithms for exact and approximate posterior sampling of both observations and partitions, and illustrate our results on predictive problems with synthetic and real data.Comment: 30 pages, 8 figure

Experimental Investigation and Thermodynamic Assessment of Phase Equilibria in the PLLA/Dioxane/Water Ternary System for Applications in the Biomedical Field

Fundamental understanding of thermodynamic of phase separation plays a key role in tuning the desired features of biomedical devices. In particular, phase separation of ternary solution is of remarkable interest in processes to obtain biodegradable and biocompatible architectures applied as artificial devices to repair, replace, or support damaged tissues or organs. In these perspectives, thermally induced phase separation (TIPS) is the most widely used technique to obtained porous morphologies and, in addition, among different ternary systems, polylactic acid (PLLA)/dioxane/water has given promising results and has been largely studied. However, to increase the control of TIPS-based processes and architectures, an investigation of the basic energetic phenomena occurring during phase separation is still required. Here we propose an experimental investigation of the selected ternary system by using isothermal titration calorimetric approach at different solvent/antisolvent ratio and a thermodynamic explanation related to the polymer-solvents interactions in terms of energetic contribution to the phase separation process. Furthermore, relevant information about the phase diagrams and interaction parameters of the studied systems are furnished in terms of liquid-liquid miscibility gap. Indeed, polymer-solvents interactions are responsible for the mechanism of the phase separation process and, therefore, of the final features of the morphologies; the knowledge of such data is fundamental to control processes for the production of membranes, scaffolds and several nanostructures. The behavior of the polymer at different solvent/nonsolvent ratios is discussed in terms of solvation mechanism and a preliminary contribution to the understanding of the role of the hydrogen bonding in the interface phenomena is also reported. It is the first time that thermodynamic data of a ternary system are collected by mean of nano-isothermal titration calorimetry (nano-ITC). Supporting Information is available

Impact of Lorentz Violation Models on Exoplanets’ Dynamics

Many exoplanets have been detected by the radial velocity method, according to which the motion of a binary system around its center of mass can produce a periodical variation of the Doppler effect of the light emitted by the host star. These variations are influenced by both Newtonian and non-Newtonian perturbations to the dominant inverse-square acceleration; accordingly, exoplanetary systems lend themselves to testing theories of gravity alternative to general relativity. In this paper, we consider the impact of the Standard Model Extension (a model that can be used to test all possible Lorentz violations) on the perturbation of radial velocity and suggest that suitable exoplanets’ configurations and improvements in detection techniques may contribute to obtaining new constraints on the model parameters