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.     

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.     

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.     

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 Challenge of Agricultural Sustainability for Asia and Europe

It is expected that climate change (CC), growing population, increasing urbanization and improving living standards are amongst the major drivers influencing future agricultural development needs. Under conventional agricultural systems, the main sources of growth in crop production are well known. Beyond suffering its consequences, agriculture has been shown to act as a driver of CC, primarily through the production and release of about 15% of the atmospheric greenhouse gases but also by altering the resilience of the agro-ecosystems. The main criterion for the production systems called conservation agriculture (CA) is the provision of an optimum environment in the root-zone to maximum possible depth. Under CA the water-holding capacity of the soil increases, and water losses are reduced. CA combined with other complementary techniques can also help reduce the emissions for methane and nitrous oxides and at the same time it can improve rural and socio-economic development.     

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.     

“Beyond GDP” Effects on National Subjective Well-Being of OECD Countries

Some authors claim that maximizing subjective well-being is a more meaningful social objective than maximizing GDP and that other factors beyond income play a major role in defining well-being. In this work, we study two issues connected with this claim, looking at the context of OECD member countries. We look at the crowded category of proposed, “beyond GDP” policy-controlled factors, searching for evidence that some might be major determinants of national average subjective well-being. We also seek to compare any such effect with that of GDP, in order to evaluate if these factors have a better chance of leading to a maximization of well-being than GDP itself. In our analyses, we make use of partial order methods that have been rarely applied to this field of study. They seem particularly appropriate to the case, as well-being and its components are generally theorized as strongly multidimensional while standard modeling strategies require a great deal of compromise when working with many potential regressors and non-trivial levels of multicollinearity.