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Greatest past scientists (Aristotle, Charles Darwin, Konrad Lorenz) have declared the indissoluble link between motions and life, or biological objects psychological state. In this perspective, new technologies are a very powerful tool to measure emotional skills that refer to the abilities to regulate one’s thoughts, emotions and behaviour. We develop a new approach to the identification of emotional skills based on description and analysis of the human energetic field and the recognition of frequencies specific to different emotional states. The objective of this paper is to propose and integrate a new technological based paradigm into a protocol/method to measure social and emotional skills that are relevant for any company, organization to select human resources and to facilitate the development of this type of abilities for employees once hired. The idea is to (i) measure the energy field and to trace its movements using appropriate measuring tools based on bio-photons movements, recorded using highly sophisticated and specific cameras; and (ii) to identify the key factors that represent the main areas of importance even as defined in recent studies by the OECD by means of the language of frequencies.

In this paper, we study the relationship between the level of development of an economy and returns to different levels of education for the panel of OECD countries over the 1965-2004 period, in a club convergence framework. The connection between growth and human capital measures of primary, secondary and tertiary education in a multiple-club spatial convergence model with non linearities and spatial dependence is considered. By decomposing total schooling into its three constituent parts, we are able to evaluate their impact on regional growth without imposing homogeneous returns from each level of education. We contribute to the identification of two regimes for OECD countries, each characterized by different returns on physical and human capital accumulation and technological spillovers. We also find that the non-monotonic pattern of convergence is strongly influenced by human capital stocks and technology diffusion process is stronger in the club less close to the technological frontier.

The objective of this paper is to develop a GME formulation for the class of spatial structural equations models (S-SEM). In this respect, two innovatory as- pects are introduced: (i) the formalization of the GME estimation approach for the spatial structural equations models which accounts for spatial heterogeneity and spa- tial dependence; (ii) the extension of the methodology to a panel data framework. We also present an application of the method to real data finalized to investigate dispari- ties of unemployment rates in OECD countries over the period 1998-2006.

Information-based estimation techniques are becoming more popular in the field of Ecological Inference. Within this branch of estimation techniques, two alternative approaches can be pointed out. The first one is the Generalized Maximum Entropy (GME) approach based on a matrix adjustment problem where the only observable information is given by the margins of the target matrix. An alternative approach is based on a distributionally weighted regression (DWR) equation. These two approaches have been studied so far as completely different streams, even when there are clear connections between them. In this paper we present these connections explicitly. More specifically, we show that under certain conditions the generalized cross-entropy (GCE) solution for a matrix adjustment problem and the GME estimator of a DWR equation differ only in terms of the a priori information considered. Then, we move a step forward and propose a composite estimator that combines the two priors considered in both approaches. Finally, we present a numerical experiment and an empirical application based on Spanish data for the 2010 year.

This book presents tools and principles of information theory as a solution to analyse insufficient information for inference, model and theory building. Info-metrics is a term that was coined in 2009 by the author for an approach based on maximum entropy for constrained optimization. This book is a good mixture of theory and practical applications. It could be of interest for researchers, university professors and graduate studentsintheareaofprobabilityandstatisticsand their applications, such as neural networks, data mining and ‘big data’ analysis.

none 3 no This paper assesses whether there might be complementarities between different types of innovation activities (product, process and organizational) and how these effects may be linked to the likelihood that a firm will export. Complementarity is addressed through the properties of supermodular functions, and firm heterogeneity by export destination is explored. A new econometric strategy to test for pairwise complementarity in a function with three independent variables and a binary dependent variable is proposed. Exogenous and endogenous innovation variables are considered by using bootstrapping for hypothesis testing, propensity score matching and treatment effects models. The empirical analysis shows that complementarity relationships between innovation strategies are more likely to exist when firms export to multiple foreign markets. mixed Bernardini Papalia, R.; Bertarelli, S.; Mancinelli, S. Bernardini Papalia, R.; Bertarelli, S.; Mancinelli, S.

The focus of this paper is on spatial structural equation models (S-SEM) also extended to a Panel data framework. More specifically, our objective is to introduce a generalized maximum entropy formulation for the class of S-SEM with the aim of developing a composite index. We present an application of the method to real data finalized to investigate dynamics and complex interactions between some selected dimensions that represent the main measures of intangible assets for a panel of OECD countries over the period 1998–2008.

The aim of this paper is to formulate an analytical–informational–theoretical approach which, given the incomplete nature of the available micro-level data, can be used to provide disaggregated values of a given variable. A functional relationship between the variable to be disaggregated and the available variables/indicators at the area level is specified through a combination of different macro- and micro-data sources. Data disaggregation is accomplished by considering two different cases. In the first case, sub-area level information on the variable of interest is available, and a generalized maximum entropy approach is employed to estimate the optimal disaggregate model. In the second case, we assume that the sub-area level information is partial and/or incomplete, and we estimate the model on a smaller scale by developing a generalized cross-entropy-based formulation. The proposed spatial-disaggregation approach is used in relation to an Italian data set in order to compute the value-added per manufa...

This paper deals with heterogeneity and nonlinearities in the growth process by developing a two-stage strategy to identify and estimate a club convergence model with threshold externalities. Because of identification and collinearity problems, we develop an entropy-based estimation procedure which simultaneously takes account of ill-posed and ill-conditioned inference problems. First, clubs are identified by introducing a mapping structure in a conditional convergence model. Finally, we estimate a multiple club convergence model, where clubs correspond to subsets of total observations. Our procedure is applied to assess the existence of club convergence for a large sample of countries (1965–2008).

Flow data are viewed as cross-classified data, and spatial interaction models are reformulated as log-linear models. According to this view, we introduce a spatial panel data model and we derive a Generalized Maximum Entropy – based estimation formulation. The estimator we propose has the advantage of being consistent with the underlying data generation process and eventually with the restrictions implied by some non sample information or by past empirical evidence by also controlling for collinearity and endogeneity problems.

This paper assess the existence of club convergence across OECD over the period 1965-2004 by developing a two stage strategy, which employs information on clustering schemes - identified by a mapping analysis - and estimates a multiple equation dynamic panel model with non linearities and spatial dependence. Because of identification and collinearity problems, we introduce an entropy-based estimation procedure which simultaneously takes account of ill-posed and ill-conditioned inference problems. At the first stage, unobserved total factor productivity differentials across OECD countries are identified by specifying a mapping structure in a convergence model with non linearities and spatial dependence. At the second step of the analysis, we estimate a two-club spatial convergence model, where clubs correspond to subsets of total observations, as identified at the first stage of the analysis.

- Information theoretic estimators do not require specification of the specific parametric functional form of sampling distributions or likelihood functions but it is only necessary to make mild assumptions concerning the existence of zero-valued moment conditions. This work aims at developing an entropy-based estimation strategy of a dynamic spatial heteroge-neous panel data model where separate processes for each unit are considered. An empirical application is provided to demonstrate practical implementation of the GME estimator when one has to deal with estimation of ill-posed or ill-conditioned models in analyzing spatial structures. Keywords: - Dynamic panel data, Generalized maximum entropy estimation, Spatial models

The objective of this paper is to test for the importance of local agglomeration externalities in determining inward foreign direct investment (FDI) intensity, viewed as a measure of regional attractiveness to FDI. The links between the degree of FDI inflow penetration into Italy and its determinants at the regional level are examined using alternative fixed effects panel data model specifications, also extended to include spatial effects. It is found that sectoral and regional specificities are relevant in attracting inward FDI into Italy and the different effects of intra‐sector and inter‐sector spillovers can be distinguished. There is also evidence that the importance of agglomeration spillovers connected to the geographical distance varies across regions, indicating both substitution and complementarity effects from FDI inflows into neighboring regions.

In this study we examine regional data on per worker GDP, disaggregated at sectoral level, by focusing our interest on the role of differences in the sectoral composition of activities, and in productivity gaps that are uniform across sectors, in explaining the catching-up process, which is realized through physical and human capital as well as technological knowledge accumulation. Our objective is to investigate how much of the interregional inequality in aggregate productivity per worker is imputable to each component. A methodology for identifying and analyzing sources of inequality from a decomposed perspective is developed in the growth framework by combining a shift-share based technique and a SUR model specification for the conditional-convergence analysis. The proposed approach is employed to analyze aggregate interregional inequality of per worker productivity levels in Italy over the period 1970–2000. With respect to the existing empirical results, our approach provides a more comprehensive and detailed examination of the contribution of each identified component in explaining the regional productivity gaps in Italy. It is argued that region-specific productivity differentials, uniform across sectors, explain a quite large share of differences in productivity per worker. However, sectoral composition plays a non negligible role, although decreasing since the end of 1980s, and very different productivity patterns emerge within geographical areas.