On tail index estimation based on multivariate data

This article is devoted to the study of tail index estimation based on i.i.d. multivariate observations, drawn from a standard heavy-tailed distribution, that is, of which Pareto-like marginals share the same tail index. A multivariate central limit theorem for a random vector, whose components correspond to (possibly dependent) Hill estimators of the common tail index α, is established under mild conditions. We introduce the concept of (standard) heavy-tailed random vector of tail index α and show how this limit result can be used in order to build an estimator of α with small asymptotic mean squared error, through a proper convex linear combination of the coordinates. Beyond asymptotic results, simulation experiments illustrating the relevance of the approach promoted are also presented.     

A new count model generated from mixed Poisson transmuted exponential family with an application to health care data

In this article, a new mixed Poisson distribution is introduced. This new distribution is obtained by utilizing mixing process, with Poisson distribution as mixed distribution and Transmuted Exponential as mixing distribution. Distributional properties like unimodality, moments, over-dispersion, infinite divisibility are studied. Three methods viz. Method of moment, Method of moment and proportion, and Maximum-likelihood method are used for parameter estimation. Further, an actuarial application in context of aggregate claim distribution is presented. Finally, to show the applicability and superiority of proposed model, we discuss count data and count regression modeling and compare with some well established models.     

Imputation of repeatedly observed multinomial variables in longitudinal surveys

It is now a standard practice to replace missing data in longitudinal surveys with imputed values, but there is still much uncertainty about the best approach to adopt. Using data from a real survey, we compared different strategies combining multiple imputation and the chained equations method, the two main objectives being (1) to explore the impact of the explanatory variables in the chained regression equations and (2) to study the effect of imputation on causality between successive waves of the survey. Results were very stable from one simulation to another, and no systematic bias did appear. The critical points of the method lied in the proper choice of covariates and in the respect of the temporal relation between variables.     

Bootstrap Prediction Intervals for Factor Models

We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the innovations. The bootstrap allows us to relax this assumption and to construct valid prediction intervals under more general conditions. Moreover, even under Gaussianity, the bootstrap leads to more accurate intervals in cases where the cross-sectional dimension is relatively small as it reduces the bias of the ordinary least-squares (OLS) estimator.     

Social innovation indicators

This article provides the results of the Regional Social Innovation Index (RESINDEX), a conceptual and empirical model that explores indicators of social innovation at organizational and regional level. The RESINDEX model links absorptive capacity of knowledge and social innovation in four types of key regional agents: profit and non-profit organizations, universities, and technological centres. The pilot application of the RESINDEX model held in the Basque Country (Spain) during the year 2013. Within the framework of this study 282 regional organizations were surveyed. The article contributes to the conceptual and empirical discussion on indicators in social innovation.     

Quasi-likelihood ratio tests for cointegration,cobreaking, and cotrending

We consider a set of variables with two types of nonstationary features, stochastic trends and broken linear trends. We develop tests that can determine whether there is a linear combination of these variables under which the nonstationary features can be canceled out. The first test can determine whether stochastic trends can be eliminated and thus whether cointegration holds, regardless of whether structural breaks in linear trends are eliminated. The second test can determine whether both stochastic trends and breaks in linear trends are simultaneously removed and thus whether cointegration and cobreaking simultaneously hold. The third test can determine whether not only breaks in linear trends but also linear trends themselves are eliminated along with stochastic trends and thus whether both cointegration and cotrending hold.     

The “wrong skewness” problem in stochastic frontier models: A new approach

Stochastic frontier models are widely used to measure, e.g., technical efficiencies of firms. The classical stochastic frontier model often suffers from the empirical artefact that the residuals of the production function may have a positive skewness, whereas a negative one is expected under the model, which leads to estimated full efficiencies of all firms. We propose a new approach to the problem by generalizing the distribution used for the inefficiency variable. This generalized stochastic frontier model allows the sample data to have the wrong skewness while estimating well-defined and nondegenerate efficiency measures. We discuss the statistical properties of the model, and we discuss a test for the symmetry of the error term (no inefficiency). We provide a simulation study to show that our model delivers estimators of efficiency with smaller bias than those of the classical model even if the population skewness has the correct sign. Finally, we apply the model to data of the U.S. textile industry for 1958–2005 and show that for a number of years our model suggests technical efficiencies well below the frontier while the classical one estimates no inefficiency in those years.     

Structural and contextual approaches to ambidexterity: A meta-analysis of organizational and environmental contingencies

This meta-analysis examines the conditions under which structural and contextual approaches help balance exploration and exploitation. Drawing on heterogeneous samples of prior ambidexterity studies, we apply moderated meta-analytic regression methods to 33,492 organizations sampled in 114 primary studies from 1991 to 2017 to test a contingency model. Our findings suggest that structural separation helps firms of all sizes to balance exploration and exploitation, and that structural separation is more conducive for balancing exploration and exploitation in high technology environments. Also, avoiding a structural separation approach benefits service firms. As research on ambidexterity enters the maturity stage we discuss the implications for future theory development, methodology, and for managers interested in developing ambidextrous organizations.     

Social mobilizations and the question of social justice in contemporary Russia

ABSTRACT

Notwithstanding stereotypes of Russian apathy, long-term field research reveals that there have always been grassroots and labour protests in post-Soviet Russia, even as the shock of ultraliberal reforms led to mass precariousness and social disorientation. However, the social mobilizations that do occur are scattered, weakly publicized and mostly small-scale. This paper conceptualizes them as ‘everyday activism’, that is, an activism embedded in everyday life experience and pragmatic sense. Only recently, and in a paradoxical relation to the populist and patriotic Kremlin discourse, some new trends have emerged towards other popular variants of the new discourse that includes social equality claims and what the paper calls ‘social critical’ populism. However, this populism from below does not automatically lead to mass mobilization, although it provides the necessary background for it.