Exit or Voice? The Recent Drivers of Kosovar Out‐migration

A remarkable out‐migration from Kosovo occurred after the winter 2014/15, raising urgent questions about its underlying drivers and implications for both Kosovo and the destination countries. This article aims at providing a better understanding of key migration triggers and some particularities of the phenomenon. We link our empirically found migration drivers to Hirschman's ( 1970 ) famous exit, voice or loyalty scheme by asking in how far the exit must be understood as the explicit alternative to voicing dissatisfaction with the current situation in the country. According to our results, the recent Kosovar out‐migration is a clear response to weak governance and thus goes beyond the widespread ‘migration‐cum‐remittances’ livelihood practice. In this sense it could be interpreted as a revolt against the political system. With view to policy implications for destination countries, we point at legal travel and migration opportunities as the better solution to channel both voice and exit.     

Bayesian Identification of Multivariate Autoregressive Processes

Identification is one of the most important stages of a time series analysis. This paper develops a direct Bayesian technique to identify the order of multivariate autoregressive processes. By employing the conditional likelihood function and a matrix normal-Wishart prior density, or Jeffrey' vague prior, the proposed identification technique is based on deriving the exact posterior probability mass function of the model order in a convenient form. Then one may easily evaluate the posterior probabilities of the model order and choose the order that maximizes the posterior mass function to be the suitable order of the time series data being analyzed. Assuming the bivariate autoregressive processes, a numerical study, with different prior mass functions, is carried out to assess the efficiency of the proposed technique. The analysis of the numerical results supports the adequacy of the proposed technique in identifying the orders of multivariate autoregressive processes.     

Some remarks on structural inference applied to weibull distribution

The structural probability distribution of the parameters of the two-parameter Weibull distribution is derived directly from considerations of the group structure of its density function. In the process we compare the structural method of inference with the confidence interval approach and reveal their similarities and differences. Structural prediction densities of arbitrary ordered statistics from Weibull distributions are also given to complement a previous work by Bury and Burnholtz.     

Promoting an endogenous approach to education at the local community level

A major postulate of this paper is that education is a subordinate component in a larger system of societies’ development. This means that any educational policy for any country should stem from a long‐term strategy to support this aim. The main focus of the study that this paper is based on is to develop a theoretical framework that identifies an educational model which would support endogenous development at the local community level. This framework is ‘indigenous community‐based education’. It is analysed in terms of its indigenous features and sharing applications, with reference to actual experiences of communities who have undertaken this type of programme.     

A brand share model for Canadian cigarettes

In an attempt to gain some insight into the way in which advertising affects the distribution of sales with different brands, a brand share model for Canadian filter cigarette brands is presented, using Canadian data for the period 1967-1975.     

On structural predictive distribution with type II progressively censored Weibull data

This note applies the structural method of inference to derive a posterior distribution of Weibull parameters and to obtain predictive probability distributions of a set of future ordered failure times \(Y_{(n_1 )}< Y_{(n_2 )}< ...< Y_{(n_k )} \) from N future observations based on a set of type-II progressively censored sample data from a two-parameter Weibull population. In particular, a predictive distribution of the k^th future failure time is given in an integral form. A brief review of the literature on these topics is also given.     

Bayesian Identification of Moving Average Models

This study approaches the Bayesian identification of moving average processes using an approximate likelihood function and a normal gamma prior density. The marginal posterior probability mass function of the model order is developed in a convenient form. Then one may investigate the posterior probabilities over the grid of the order and choose the order with the highest probability to solve the identification problem. A comprehensive simulation study is carried out to demonstrate the performance of the proposed procedure and check its adequacy in handling the identification problem. In addition, the proposed Bayesian procedure is compared with some non Bayesian automatic techniques and another Bayesian technique. The numerical results support the adequacy of using the proposed procedure in solving the identification problem of moving average processes.