Between Transition and Enlargement: The Composition of Consumption in European Households

The article aims to specifically consider the effect of the enlargement of markets, resulting from the process of transition in Central and Eastern European countries, on the structure of household consumption. On the basis of a discriminatory analysis performed on recent data published by OECD and Eurostat, this study proposes to address three main questions: if during transition the household consumption patterns, measured by budget shares, have changed; if from this point of view, Central and Eastern European countries are similar between them and diverge from Western ones; if today there are any signs of convergence between the two groups of countries. The dissimilarity in budget share in Eastern and Western countries is proven and persistent, appearing greater in real terms than in nominal ones. On the one hand, Engel’s law is confirmed, given that at lower income levels the percentage of food consumption is greater, even though in real terms the absolute level of consumption of food has, in most cases, been reduced. On the other hand, the historical experience of Eastern countries is evident in the prolonged productive-distributive role of the State, especially in the field of essential services: home, schooling, and health.     

Boundaries of the business model within business groups

Scholars have sought various ways to find out how financial performance of the firm can be affected by its business model (BM). However, to date academic literature has focused attention on the “firm” as a unit of analysis without clearly defining the boundaries of the reporting entity to which the BMs refer. The aim of this paper is to investigate what are the boundaries of the BM of the affiliated-group companies and how the degree of independence of BMs is measured within the business group. The contribution of the paper is in using the BM concept to expound and criticise the assumptions in economic analysis and accounting standards that groups of companies are economic units that optimise economic income of the group as a whole and that the financial statements of individual subsidiaries, sub-groups and the group as a whole report the value generated by the group.     

An exact semidefinite programming approach for the max-mean dispersion problem

This paper proposes an exact algorithm for the Max-Mean dispersion problem (\(Max-Mean DP\)), an NP-Hard combinatorial optimization problem whose aim is to select the subset of a set such that the average distance between elements is maximized. The problem admits a natural non-convex quadratic fractional formulation from which a semidefinite programming (SDP) relaxation can be derived. This relaxation can be tightened by means of a cutting plane algorithm which iteratively adds the most violated triangular inequalities. The proposed approach embeds the SDP relaxation and the cutting plane algorithm into a branch and bound framework to solve \(Max-Mean DP\) instances to optimality. Computational experiments show that the proposed method is able to solve to optimality in reasonable time instances with up to 100 elements, outperforming other alternative approaches.     

Social capital as social networks: A new framework for measurement and an empirical analysis of its determinants and consequences

The contribution of this paper to the social capital literature is threefold. First, we set up a new framework for measurement, allowing us to build indicators for five different dimensions of the concept. Second, we provide a single, synthetic, measure capturing that particular configuration of social capital which the literature generally associates with positive economic outcomes. Third, we carry out an empirical assessment of the relationships between the different types of social capital identified by our analysis and a range of socio-economic phenomena.     

Outlier detection in contingency tables based on minimal patterns

A new technique for the detection of outliers in contingency tables is introduced, where outliers are unusual cell counts with respect to classical loglinear Poisson models. Subsets of cell counts called minimal patterns are defined, corresponding to non-singular design matrices and leading to potentially uncontaminated maximum-likelihood estimates of the model parameters and thereby the expected cell counts. A criterion to easily produce minimal patterns in the two-way case under independence is derived, based on the analysis of the positions of the chosen cells. A simulation study and a couple of real-data examples are presented to illustrate the performance of the newly developed outlier identification algorithm, and to compare it with other existing methods.     

On implementation of the Gibbs sampler for estimating the accuracy of multiple diagnostic tests

Implementation of the Gibbs sampler for estimating the accuracy of multiple binary diagnostic tests in one population has been investigated. This method, proposed by Joseph, Gyorkos and Coupal, makes use of a Bayesian approach and is used in the absence of a gold standard to estimate the prevalence, the sensitivity and specificity of medical diagnostic tests. The expressions that allow this method to be implemented for an arbitrary number of tests are given. By using the convergence diagnostics procedure of Raftery and Lewis, the relation between the number of iterations of Gibbs sampling and the precision of the estimated quantiles of the posterior distributions is derived. An example concerning a data set of gastro-esophageal reflux disease patients collected to evaluate the accuracy of the water siphon test compared with 24 h pH-monitoring, endoscopy and histology tests is presented. The main message that emerges from our analysis is that implementation of the Gibbs sampler to estimate the parameters of multiple binary diagnostic tests can be critical and convergence diagnostic is advised for this method. The factors which affect the convergence of the chains to the posterior distributions and those that influence the precision of their quantiles are analyzed.     

Illegal Immigrants and the Informal Economy in Italy

ABSTRACT: The present work is divided into three parts. In the first part the (limited) empirical data relevant to the phenomenon in question is presented, that is, the growing illegality of Italian immigration and the illegal work that immigrants are destined to take up. Obviously the illegality of their presence and the íllicitness of the work are, by definition, phenomena which are difficult to quantify. Some estimates, albeit limited to certain regions, are nevertheless available and the picture that emerges is coherent with the present hypothesis. The hypothesis is then explained at a theoretical level, in the second part, where a simple model of general equilibrium is presented, referring to a two-sector economy with a single good. The model has the sole aim of illustrating the plausible characteristics of an economy characterised by the phenomenon that this present paper attempts to clarify. In the third part, to give an example, a simulation is put forward, utilising, in the model, values not incoherent with estimates based on Italy as a whole.     

Robust estimation in long-memory processes under additive outliers

In this paper, we introduce an alternative semiparametric estimator of the fractional differencing parameter in ARFIMA models which is robust against additive outliers. The proposed estimator is a variant of the GPH estimator [Geweke, J., Porter-Hudak, S., 1983. The estimation and application of long memory time series model. Journal of Time Series Analysis 4, 221–238]. In particular, we use the robust sample autocorrelations of Ma, Y. and Genton, M. [2000. Highly robust estimation of the autocovariance function. Journal of Time Series Analysis 21, 663–684] to obtain an estimator for the spectral density of the process. Numerical results show that the estimator we propose for the differencing parameter is robust when the data contain additive outliers.     

Mixing and moments properties of a non-stationary copula-based Markov process

Abstract

We provide conditions under which a non-stationary copula-based Markov process is geometric β-mixing and geometric ρ-mixing. Our results generalize some results of Beare who considers the stationary case. As a particular case we introduce a stochastic process, that we call convolution-based Markov process, whose construction is obtained by using the C-convolution operator which allows the increments to be dependent. Within this subclass of processes we characterize a modified version of the standard random walk where copulas and marginal distributions involved are in the same elliptical family. We study mixing and moments properties to identify the differences compared to the standard case.     

Perceptions about Compliant Flooring from Senior Managers in Long-Term Care

This study explored barriers and facilitators to adoption of compliant flooring as a fall injury prevention strategy within long-term care from the perspective of 18 long-term care senior managers. In-depth interviews were recorded, transcribed, and analyzed using the Framework Method. The most important organizational facilitators to adoption were potential for injury prevention and long-term care staff's openness to change. The most important organizational barriers to adoption were negative effects to long-term care staff and financial considerations (i.e., cost and lack of funding). The most important general organizational considerations were uncertainties about clinical effectiveness, effects on long-term care staff, and flooring performance. Overall, compliant flooring was viewed positively for long-term care. The findings also suggest an opportunity for knowledge translation to inform long-term care senior managers about the existing evidence on compliant flooring.