Muslim Immigrants in Greece: Religious Organization and Local Responses

Muslims are the second largest religious group in Greece. In this essay their migration process, numbers, settlement patterns and demographic characteristics are presented. It is shown that Muslims in Greece include not only economic migrants but also an indigenous population residing in the northern part of the country. The essay discusses mainly the immigrants' efforts to establish associations and prayer halls as well as the state's initiative to establish a central mosque in the suburbs of Athens. It argues that although Muslims are now an integral part of a multi-religious Greece, there is a long way to go to achieving their integration.     

Joint modeling of longitudinal continuous,longitudinal ordinal,and time-to-event outcomes

Lifetime Data Analysis - In this paper, we propose an innovative method for jointly analyzing survival data and longitudinally measured continuous and ordinal data. We use a random effects...     

Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data

Summary.  A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes the consideration of high dimensional random-effects structures feasible. Contrary to the standard Laplace approximation, our method requires much fewer repeated measurements per individual to produce reliable results.     

A bootstrap test for time series linearity

A bootstrap algorithm is proposed for testing Gaussianity and linearity in stationary time series, and consistency of the relevant bootstrap approximations is proven rigorously for the first time. Subba Rao and Gabr (1980) and Hinich (1982) have formulated some well-known nonparametric tests for Gaussianity and linearity based on the asymptotic distribution of the normalized bispectrum. The proposed bootstrap procedure gives an alternative way to approximate the finite-sample null distribution of such test statistics. We revisit a modified form of Hinich's test utilizing kernel smoothing, and compare its performance to the bootstrap test on several simulated data sets and two real data sets—the S&P 500 returns and the quarterly US real GNP growth rate. Interestingly, Hinich's test and the proposed bootstrapped version yield substantially different results when testing Gaussianity and linearity of the GNP data.     

Bootstrap confidence intervals in mixtures of discrete distributions

The problem of building bootstrap confidence intervals for small probabilities with count data is addressed. The law of the independent observations is assumed to be a mixture of a given family of power series distributions. The mixing distribution is estimated by nonparametric maximum likelihood and the corresponding mixture is used for resampling. We build percentile-t and Efron percentile bootstrap confidence intervals for the probabilities and we prove their consistency in probability. The new theoretical results are supported by simulation experiments for Poisson and geometric mixtures. We compare percentile-t and Efron percentile bootstrap intervals with eight other bootstrap or asymptotic theory based intervals. It appears that Efron percentile bootstrap intervals outperform the competitors in terms of coverage probability and length.     

KNOWLEDGE SPILLOVERS AND OUTPUT PER WORKER: AN INDUSTRY‐LEVEL ANALYSIS FOR OECD COUNTRIES

This study analyzes the impact of knowledge spillovers on output per worker at the industry level using a primal production function approach. The article makes three different contributions to the international spillovers literature: (1) it identifies trade‐related spillovers under alternative assumptions regarding the information transferred through imports; (2) it explores the importance of horizontal and vertical foreign direct investment (FDI) in knowledge spillovers; and (3) it looks at how institutional factors determine the impact of FDI‐related spillovers on productivity. The main findings of the study are: (1) international knowledge spillover is an important driver of industry output per worker, and the magnitude of this spillover effect varies with alternative assumptions about the information content embodied in imports, while high technology industries benefit significantly more from import‐related knowledge spillovers; and (2) the gains from FDI spillovers are primarily horizontal, but when institutional factors are considered, countries with stronger protection of intellectual property rights and a high “ease of doing business” tend to experience a substantial increase in the effectiveness of both horizontal and vertical FDI‐related spillovers. (JEL E24, F1, F6, O3, O4)     

US labour unions and green transitions: depth,breadth, and worker agency

ABSTRACT

The goal of this article is to interpret and analyse two ambitious and prominent cases of labour environmentalism in the USA – the par excellence example of liberal capitalism. In order to capture the nuances, contradictions, and varieties of labour environmentalism, I employ an analytical scheme based on three dimensions. The first dimension (depth) explores the social and environmental commitments manifested in these cases, the second dimension (breadth) explores their spatiality, and the third dimension (agency) explores the degree to which these initiatives reflect a proactive as opposed to a reactive labour environmentalism.     

Residual‐Based Block Bootstrap for Unit Root Testing

A nonparametric, residual‐based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series. The resampling procedure is based on weak assumptions on the dependence structure of the stationary process driving the random walk and successfully generates unit root integrated pseudo‐series retaining the important characteristics of the data. It is more general than previous bootstrap approaches to the unit root problem in that it allows for a very wide class of weakly dependent processes and it is not based on any parametric assumption on the process generating the data. As a consequence the procedure can accurately capture the distribution of many unit root test statistics proposed in the literature. Large sample theory is developed and the asymptotic validity of the block bootstrap‐based unit root testing is shown via a bootstrap functional limit theorem. Applications to some particular test statistics of the unit root hypothesis, i.e., least squares and Dickey‐Fuller type statistics are given. The power properties of our procedure are investigated and compared to those of alternative bootstrap approaches to carry out the unit root test. Some simulations examine the finite sample performance of our procedure.     

Exact Bayesian modeling for bivariate Poisson data and extensions

Bivariate count data arise in several different disciplines (epidemiology, marketing, sports statistics just to name a few) and the bivariate Poisson distribution being a generalization of the Poisson distribution plays an important role in modelling such data. In the present paper we present a Bayesian estimation approach for the parameters of the bivariate Poisson model and provide the posterior distributions in closed forms. It is shown that the joint posterior distributions are finite mixtures of conditionally independent gamma distributions for which their full form can be easily deduced by a recursively updating scheme. Thus, the need of applying computationally demanding MCMC schemes for Bayesian inference in such models will be removed, since direct sampling from the posterior will become available, even in cases where the posterior distribution of functions of the parameters is not available in closed form. In addition, we define a class of prior distributions that possess an interesting conjugacy property which extends the typical notion of conjugacy, in the sense that both prior and posteriors belong to the same family of finite mixture models but with different number of components. Extension to certain other models including multivariate models or models with other marginal distributions are discussed.