One-sided synthetic control charts for monitoring the multivariate coefficient of variation

Shewhart's type control charts for monitoring the Multivariate Coefficient of Variation (MCV) have recently been proposed in order to monitor the relative variability compared with the mean. These approaches are known to be rather slow in the detection of small or moderate process shifts. In this paper, in order to improve the detection efficiency, two one-sided Synthetic charts for the MCV are proposed. A Markov chain method is used to evaluate the statistical performance of the proposed charts. Furthermore, computational experiments reveal that the proposed control charts outperform the Shewhart MCV control chart in terms of the average run length to detect an out-of-control state. Finally, the implementation of the proposed chart is illustrated with an example using steel sleeves data.     

WHERE ARE THE ECONOMISTS WHO PUBLISH? PUBLICATION CONCENTRATION AND RANKINGS IN EUROPE BASED ON CUMULATIVE PUBLICATIONS

We measure the past production of research articles by current members of European economics institutions. All EconLit journals are used, weighted to reflect differences in quality. Both a long (1971–2000) and a short (1996–2000) time period are considered. We also provide production indices that take into account the authors' career length. The total output of each research center is measured as well as its production per member. The focus is on 600 centers from eighteen European countries (EU 14, Israel, Norway, Switzerland, and Turkey). European centers are compared to the top sixty U.S. economics departments. Statistics regarding the concentration of article production across researchers, institutions, and countries are provided, as well as on publication habits. (JEL: A14, L11, R32)     

Solving hybrid flow shop problem using energetic reasoning and global operations

This paper shows that the use of satisfiability tests and time-bound adjustments based on energetic reasoning and global operations can enhance the efficiency of branch and bound procedures for optimally solving the hybrid flow shop scheduling problem. Procedures to use energetic reasoning and extended forms of global adjustment techniques are described and are empirically evaluated. Computational results show that the proposed approaches outperform the best known procedures to optimally solve the hybrid flow shop problem.     

Multiple Kernel Procedure: an Asymptotic Support

ABSTRACT. This paper deals with kernel non-parametric estimation. The multiple kernel method, as proposed by Berlinet (1993), consists in choosing both the smoothing parameter and the order of the kernel function. In this paper we follow this general idea, and the selection is carried out by a combination of plug-in and cross-validation techniques. In a first attempt we give an asymptotic optimality theorem which is stated in a general unifying setting that includes many curve estimation problems. Then, as an illustration, it will be seen how this behaves in both special cases of kernel density and kernel regression estimation.     

The Impact of Technological and Organizational Changes on Labor Flows. Evidence on French Establishments

Abstract. This paper investigates the effects of organizational and technological changes on job stability of different occupational categories in France. We conduct an empirical analysis in which we make extensive use of a unique data set on a representative sample of French establishments. Working with various indicators of labor flows (gross labor flows, hiring rate, firing rate, net labor flows, and churning flows), we find that the use of new technology seems to have a positive effect on aggregate job turnover and, more specifically, turnover among manual workers. In contrast, innovative workplace organizational practices are related to lower turnover among clerical workers and intermediate professionals and have a positive effect on churning among managers.     

Estimating Long Memory in Volatility

We consider semiparametric estimation of the memory parameter in a model that includes as special cases both long‐memory stochastic volatility and fractionally integrated exponential GARCH (FIEGARCH) models. Under our general model the logarithms of the squared returns can be decomposed into the sum of a long‐memory signal and a white noise. We consider periodogram‐based estimators using a local Whittle criterion function. We allow the optional inclusion of an additional term to account for possible correlation between the signal and noise processes, as would occur in the FIEGARCH model. We also allow for potential nonstationarity in volatility by allowing the signal process to have a memory parameter d^*1/2. We show that the local Whittle estimator is consistent for d^*∈(0,1). We also show that the local Whittle estimator is asymptotically normal for d^*∈(0,3/4) and essentially recovers the optimal semiparametric rate of convergence for this problem. In particular, if the spectral density of the short‐memory component of the signal is sufficiently smooth, a convergence rate of n^2/5−δ for d^*∈(0,3/4) can be attained, where n is the sample size and δ>0 is arbitrarily small. This represents a strong improvement over the performance of existing semiparametric estimators of persistence in volatility. We also prove that the standard Gaussian semiparametric estimator is asymptotically normal if d^*=0. This yields a test for long memory in volatility.     

LOCATION OF PARTICULAR POINTS IN NON-PARAMETRIC REGRESSION ANALYSIS

In the random-design non-parametric regression model, the locations of particular values of the regression function or its derivatives are estimated. This paper investigates several stochastic modes of convergence and finds their rate of convergence under regularity assumptions, for a wide class of non-parametric estimators. The approach finds two natural fields of application: estimation of zeros/extrema and non-parametric absolute calibration.