Identifying social innovations in European local rural development initiatives

Social innovation is attracting increasing attention in research and policy, heightened by continuing austerity across Europe. Therefore, this paper examines earlier research into community-led local development (CLLD) initiatives in rural areas of Europe to develop our understanding of the meaning and scope of rural social innovation. We draw on a Schumpeterian view where innovations emerge from new combinations of resources that bring about positive changes and create value in society. A Schumpeterian social innovation framework is derived as the basis for re-analysing data from previous evaluations of LEADER policy in five different national contexts. This elicits a clearer understanding of social innovation in a rural development context, identifying different processes and outcomes that create social value. As the CLLD agenda and the demand for innovation in Europe gather pace, our aspirations are to inform future research and other initiatives on how to integrate social innovation into the design and evaluation of new rural development policies and programmes.     

The Volatility of Realized Volatility

In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. The construction of “observable” or realized volatility series from intra-day transaction data and the use of standard time-series techniques has lead to promising strategies for modeling and predicting (daily) volatility. In this article, we show that the residuals of commonly used time-series models for realized volatility and logarithmic realized variance exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance for modeling and forecasting realized volatility. In an empirical application for S&P 500 index futures we show that allowing for time-varying volatility of realized volatility and logarithmic realized variance substantially improves the fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting.     

The Input Evaluation of Generalized Bernoulli Processes for Salary Lines Construction by Means of Continuous Time Generalized Non-Homogeneous Semi-Markov Processes

Salary line forecasting assumes a relevant role in manpower and in pension funds Previously, the authors presented a generalized Bernoulli process, useful for forecasting the evolution of salary lines, taking into account the salary costs, the number of workers at each rank and the probability transitions between the ranks. The problem with applying this model is constructing the probability of transition between the grades. In this article, we will present a model that allows obtaining these probabilities by means of the solution of the evolution equation of a generalization of continuous time non-homogeneous semi-Markov processes.     

SMEs and Marketing: A Systematic Literature Review

This paper presents a systematic review of recent academic literature analysing the role, organization and management of marketing activities in small and medium‐sized enterprises (SMEs). To this end, 310 articles published between 2006 and 2015 in 69 main journals devoted to small firms/entrepreneurship and management/marketing fields were analysed. This review shows that SMEs’ marketing has received great attention in both management and marketing literature in recent years. Findings reveal, on the one hand, the emerging role of networks and information and communication technologies in marketing behaviour by SMEs, and on the other hand a research gap in terms of specific marketing practices. Entrepreneurial marketing has been used as the main conceptual framework in reviewed studies, even if findings overall still point out a distance between the theoretical bases of reviewed contributions and the study of SMEs’ marketing behaviour and practices. Therefore, future research on the role of resources, relationships and networks could benefit from the combination of theories developed within the field of entrepreneurship with other approaches such as the resource‐based view, the dynamic capabilities theory and the Industrial Marketing and Purchasing (IMP) framework.     

A generalized predictive criterion for model selection

Given a random sample from some unknown model belonging to a finite class of parametric models, assume that the estimate of the density of a future observation is of interest San Martini & Spezzaferri (1984) proposed for this problem a predictive criterion based on the logarithmic utility function. The present authors investigate a generalization of this criterion that uses as a loss function an element of the class of α‐divergences discussed by Ali & Silvey (1966) and Csiszár (1967). They also discuss briefly the case in which the class of models considered is not exhaustive.     

Bayes factors for goodness of fit testing

We propose the use of the generalized fractional Bayes factor for testing fit in multinomial models. This is a non-asymptotic method that can be used to quantify the evidence for or against a sub-model. We give expressions for the generalized fractional Bayes factor and we study its properties. In particular, we show that the generalized fractional Bayes factor has better properties than the fractional Bayes factor.     

The Volatility of Realized Volatility

In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. The construction of “observable” or realized volatility series from intra-day transaction data and the use of standard time-series techniques has lead to promising strategies for modeling and predicting (daily) volatility. In this article, we show that the residuals of commonly used time-series models for realized volatility and logarithmic realized variance exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance for modeling and forecasting realized volatility. In an empirical application for S&P 500 index futures we show that allowing for time-varying volatility of realized volatility and logarithmic realized variance substantially improves the fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting.     

Event-tree analysis with imprecise probabilities

Novel methods are proposed for dealing with event-tree analysis under imprecise probabilities, where one could measure chance or uncertainty without sharp numerical probabilities and express available evidence as upper and lower previsions (or expectations) of gambles (or bounded real functions). Sets of upper and lower previsions generate a convex set of probability distributions (or measures). Any probability distribution in this convex set should be considered in the event-tree analysis. This article focuses on the calculation of upper and lower bounds of the prevision (or the probability) of some outcome at the bottom of the event-tree. Three cases of given information/judgments on probabilities of outcomes are considered: (1) probabilities conditional to the occurrence of the event at the upper level; (2) total probabilities of occurrences, that is, not conditional to other events; (3) the combination of the previous two cases. Corresponding algorithms with imprecise probabilities under the three cases are explained and illustrated by simple examples.     

Quick estimation of tourist nights spent in Italy

ISTAT currently disseminates monthly provisional data on arrivals and nights spent in the Italian tourist establishments after 90 days from the end of the reference month, according to the EU Directive on Tourism Statistics. The aim of the paper is to compare some quick estimation methods able to improve timeliness and quality of provisional estimates. According to a super-population model, on the basis of available quick responses of provinces which tourist data are available within 45 days, some predictors are proposed in addition to that actually used. An empirical application has been carried out, using true monthly data on nights spent in Italy in 2002 and comparing predictors derived from balanced sampling theory and regression methods.     

Optimal predictive sample size for case–control studies

Summary.  The identification of factors that increase the chances of a certain disease is one of the classical and central issues in epidemiology. In this context, a typical measure of the association between a disease and risk factor is the odds ratio. We deal with design problems that arise for Bayesian inference on the odds ratio in the analysis of case–control studies. We consider sample size determination and allocation criteria for both interval estimation and hypothesis testing. These criteria are then employed to determine the sample size and proportions of units to be assigned to cases and controls for planning a study on the association between the incidence of a non-Hodgkin's lymphoma and exposition to pesticides by eliciting prior information from a previous study.