A trichotomic segmentation approach for estimating composite indicators

Composite indicators (CIs) are commonly used for benchmarking of countries over the years, summarizing in a single measurement, complex social, economic, environmental etc. concepts by involving several thematically related sub-indicators. When estimating  CIs and for a few specific countries, it is possible to have a strong indication and belief about their performance, prior to obtaining their scores. Based on that, a number of countries are likely to occupy the top-ranking positions; some will remain at the bottom list, while others may range in intermediate places. This initial preference information imposes a trichotomic segmentation that divides the countries under assessment into categories of superior, inferior and of ambiguous future performance. In this paper, we introduce the trichotomic segmentation as initial preference information to estimate the values of the CIs. We build on the popular Benefit of the Doubt (BoD) method with common weights and develop a two-goal linear programming model, that next to the evaluation of the common weights for the sub-indicators, estimates cut-off points for the CI values that distinguish the superior and inferior countries. The proposed model maintains the advantages of the common variable weighting and produces scores that induce better discrimination of the countries, having also a significant correlation with the original CI scores. The proposed methodology is applied to reassess the Digital Economy and the Society Index.

     

Sample Size Calculation for the Therapeutic Equivalence Problem

A method is proposed for the sample size calculation in the case of therapeutic equivalence of two pharmaceuticals, when the decision is based on post-treatment differences and the post-treatment values are dependent on the pretreatment ones. When the correlation coefficient is large (over 0.7), it is shown that sample size calculation (and the corresponding hypothesis test) based on the sample statistic formed by the mean difference of the post–pre differences of each group has smaller variance and hence leads to smaller sample sizes.     

Local power of panel unit root tests allowing for structural breaks

The asymptotic local power of least squares–based fixed-T panel unit root tests allowing for a structural break in their individual effects and/or incidental trends of the AR(1) panel data model is studied. Limiting distributions of these tests are derived under a sequence of local alternatives, and analytic expressions show how their means and variances are functions of the break date and the time dimension of the panel. The considered tests have nontrivial local power in a N^?1/2 neighborhood of unity when the panel data model includes individual intercepts. For panel data models with incidental trends, the power of the tests becomes trivial in this neighborhood. However, this problem does not always appear if the tests allow for serial correlation in the error term and completely vanishes in the presence of cross-section correlation. These results show that fixed-T tests have very different theoretical properties than their large-T counterparts. Monte Carlo experiments demonstrate the usefulness of the asymptotic theory in small samples.     

Generalized fixed‐T panel unit root tests

Panel data unit root tests, which can be applied to data that do not have many time series observations, are based on very restrictive error and deterministic component specification assumptions. In this paper, we develop a new, doubly modified estimator, based on which we propose a panel unit root test that allows for multiple structural breaks, linear and nonlinear trends, heteroscedasticity, serial correlation, and error cross‐section heterogeneity, when the number of time series observations is finite. The test has the additional perk that it is invariant to the initial condition.     

Data-Transformation and Test of Fit for the Generalized Pareto Hypothesis

In this article, we consider Crámer–von Mises type goodness-of-fit statistics for the Generalized Pareto law. The tests involve a certain transformation of the original observations, which, at least in the case of completely specified null distribution, may be viewed as transforming to uniformity and comparing the resulting moments of arbitrary positive order to those of a uniform distribution. The method is shown to be consistent, and the asymptotic null distribution of the test statistic is derived. Simulation results indicate that the proposed test compares well with standard methods based on the empirical distribution function.     

Characteristic function-based inference for GARCH models with heavy-tailed innovations

We consider estimation and goodness-of-fit tests in GARCH models with innovations following a heavy-tailed and possibly asymmetric distribution. Although the method is fairly general and applies to GARCH models with arbitrary innovation distribution, we consider as special instances the stable Paretian, the variance gamma, and the normal inverse Gaussian distribution. Exploiting the simple structure of the characteristic function of these distributions, we propose minimum distance estimation based on the empirical characteristic function of properly standardized GARCH-residuals. The finite-sample results presented facilitate comparison with existing methods, while the new procedures are also applied to real data from the financial market.     

That Unwanted Feeling: A Psychodynamic Study of Disappointment in Organizations

This paper explores the emotion of disappointment in organizations and develops a new line of theorizing inspired by psychoanalytic object‐relations theory. Existing literature frames disappointment as a threat to organizational effectiveness, as both a response and an anticipation of failure and as an emotion that needs to be managed in order to prevent it from damaging organizational morale and performance. This only captures part of the complexity of disappointment and leaves unexplored its potential contribution to organizational and individual learning and even creativity. The paper develops a theoretical framework which depicts disappointment in three configurations or positions, and it establishes the potential of disappointment acting as an integrative emotion within organizations. The framework accounts for an apparent contradiction in organizational members' experience of disappointment – that it is, at the same time, seen as ‘of little concern’ to individuals, and yet viewed as capable of undermining stability and destroying positive feelings. The paper shows how disappointment is connected to the dynamics of blame in organizations but, when fully appreciated, can offer a way of moving beyond these dynamics by recognizing partial failure within an organization and turning it into the basis for organizational learning.