Modelling of repairable systems with various degrees of repair

The availability of a stochastic repairable system depends on the failure behaviour and on repair strategies. In this paper, we deal with a general repair model for a system using auxiliary counting processes and corresponding intensities which include various degrees of repair (between minimal repair and perfect repair). For determining the model parameters we need estimators depending on failure times and repair times: maximum likelihood (ML) estimator and Bayes estimators are considered. Special results are obtained by the use of Weibull-type intensities and random observation times.     

The effect of entrepreneurship education in schools on entrepreneurial outcomes: a systematic review

Management Review Quarterly - This study systematically reviewed 21 quantitative and qualitative articles on the effect of entrepreneurship education in schools. We reviewed short-term effects,...     

Do foreign exchange forecasters apply asymmetric loss functions? Evidence from three major exchange rates

This article investigates which type of loss function is consistent with the hypothesis that major exchange rate forecasts, i.e. the euro, the British pound, and the Japanese yen vis-à-vis the US dollar, are rational. We apply a comprehensive data set, which also allows us to examine different forecast horizons and heterogeneity of forecasters.     

Rising wage inequality,the decline of collective bargaining,and the gender wage gap

This paper investigates the increase in wage inequality, the decline in collective bargaining, and the evolution of the gender wage gap in West Germany between 2001 and 2006. Based on detailed linked employer–employee data, we show that wage inequality is rising strongly — driven not only by real wage increases at the top of the wage distribution, but also by real wage losses below the median. Coverage by collective wage bargaining plummets by 16.5 (19.1) percentage points for male (female) employees. Despite these changes, the gender wage gap remains almost constant, with some small gains for women at the bottom and at the top of the wage distribution. A sequential decomposition analysis using quantile regression shows that all workplace related effects (firm effects and bargaining effects) and coefficients for personal characteristics contribute strongly to the rise in wage inequality. Among these, the firm coefficients effect dominates, which is almost exclusively driven by wage differences within and between different industries. Labor demand or firm wage policy related effects contribute to an increase in the gender wage gap. Personal characteristics tend to reduce wage inequality for both males and females, as well as the gender wage gap.     

Benefits of Cross‐sector Partnerships in Markets at the Base of the Pyramid

In this paper we analyze whether cross‐sector partnerships enable companies to respond to the specific conditions at the base of the pyramid (BOP). We develop three hypotheses in which we argue how cross‐sector partnerships support companies to face unfamiliar conditions in these markets. We test the developed hypotheses against the data of 103 companies operating in BOP‐markets. The results show that companies rely on organizations from the civil society sector in order to meet customer needs. Partners from the business sector are supportive when responding to restrictive market conditions. Institutional partnerships should be considered when companies aim at responding to the regulatory environment. We outline theoretical and managerial implications and reflect some limitations of the study. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.     

POSTERIOR‐PREDICTIVE EVIDENCE ON US INFLATION USING EXTENDED NEW KEYNESIAN PHILLIPS CURVE MODELS WITH NON‐FILTERED DATA

Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low‐frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time‐varying patterns in levels and volatilities. Forward‐ and backward‐looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation‐based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long‐run stability between inflation and marginal costs. Backward‐looking inflation appears stronger than forward‐looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years. Copyright © 2014 John Wiley & Sons, Ltd.