The Comparison of Performances of Widely Used Cointegration Tests

The aim of this study is to compare performances of commonly cointegration tests used in literature in terms of their empirical power and type I error probabilty for various sample sizes. As a result of the study, it has been found that some tests are not appropriate in testing cointegration in terms of empirical power and type I error probability. As a result of simulation study, λ_max test for any values of ρ and sample sizes have been found most appropriate test in conclusion.     

New Shrinkage Parameters for the Liu-type Logistic Estimators

The binary logistic regression is a widely used statistical method when the dependent variable has two categories. In most of the situations of logistic regression, independent variables are collinear which is called the multicollinearity problem. It is known that multicollinearity affects the variance of maximum likelihood estimator (MLE) negatively. Therefore, this article introduces new shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) in order to decrease the variance and overcome the problem of multicollinearity. A Monte Carlo study is designed to show the goodness of the proposed estimators over MLE in the sense of mean squared error (MSE) and mean absolute error (MAE). Moreover, a real data case is given to demonstrate the advantages of the new shrinkage parameters.     

Low wages and high unemployment rates: The role of social interactions in hiring discrimination

The purpose of this paper is to explain why low-wage workers with identical qualifications to higher-wage workers are more exposed to unemployment. Each worker is considered to belong to a social group (defined according to his/her gender, age, and nationality). We assume that workers experience both productive interdependencies and social interactions within the firm. Also inter- and intra-group interactions determine worker productivity, and frictions on the labor market limit the hiring of the most productive workers. Consequently, externalities acting both within the firm and in the labor market can lead to a higher rate of unemployment for low-wage workers.     

Stochastic Bounds with a Low Rank Decomposition

We investigate how we can bound a discrete time Markov chain (DTMC) by a stochastic matrix with a low rank decomposition. In the first part of the article, we show the links with previous results for matrices with a decomposition of size 1 or 2. Then we show how the complexity of the analysis for steady-state and transient distributions can be simplified when we take into account the decomposition. Finally, we show how we can obtain a monotone stochastic upper bound with a low rank decomposition.     

Annualized hours as a capacity planning tool in make-to-order or assemble-to-order environment: An agricultural implements company case

Capacity planning is one of the most important elements for an efficient production planning and control system. It is even more important when the main strategies to fulfil sales requirements are make-to-order (MTO) or assemble-to-order (ATO). Neither MTO or ATO companies keep finished products in stock which means that production level is driven by actual sales figures. Therefore, it is very important to have an available capacity as flexible as possible to meet sales requirements. Annualized hours (AH) is a work time control system that helps in increasing the flexibility of available capacity. The main rule of the AH system is to hire employees to work a certain number of hours on a yearly basis. Following some other pre-defined rules and limits, the AH system allows a company to adjust weekly available time in order to reduce idle capacity or to fulfil requirements that could mean overtime. This paper proposes a linear programming model to plan the operations using AH. This model has been implemented in a company that produces agricultural implements, showing that it is very simple to use. It is producing excellent practical results.     

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.     

Generic charisma - Conceptualization and measurement

In these times, when fame is available to almost anyone, it is worth being able to measure the extent and dimensions of a person's charisma. Most of the research on charisma to date is restricted to the area of leadership. In this article, however, charisma is generalized to take in all human beings and is conceptualized as a multi-dimensional cognitive-affective phenomenon. The article presents a pilot study aimed at providing an adequate operational definition of the construct as well as an initial tool for its measurement. A following literature review shows how authors have developed the concept of charisma and its initial operational dimensions for empirical research. A questionnaire is developed in three stages. The factor analyses applied in stages two and three point towards a six-factor solution (i.e. six dimensions of charisma). Further analysis reveals that the developed instrument is reliable and viable as well as applicable for future theoretical and practical work.     

Population monotonic path schemes for simple games

A path scheme for a game is composed of a path, i.e., a sequence of coalitions that is formed during the coalition formation process and a scheme, i.e., a payoff vector for each coalition in the path. A path scheme is called population monotonic if a player’s payoff does not decrease as the path coalition grows. In this study, we focus on Shapley path schemes of simple games in which for every path coalition the Shapley value of the associated subgame provides the allocation at hand. Obviously, each Shapley path scheme of a game is population monotonic if and only if the Shapley allocation scheme of the game is population monotonic in the sense of Sprumont (Games Econ Behav 2:378–394, 1990). We prove that a simple game allows for population monotonic Shapley path schemes if and only if the game is balanced. Moreover, the Shapley path scheme of a specific path is population monotonic if and only if the first winning coalition that is formed along the path contains every minimal winning coalition. We also show that each Shapley path scheme of a simple game is population monotonic if and only if the set of veto players of the game is a winning coalition. Extensions of these results to other efficient probabilistic values are discussed.