Factors Influencing Voluntary Disclosure in the Annual Reports of Portuguese Foundations

The aim of this paper is to examine the influence of Portuguese Foundation’s characteristics on their annual report disclosure practices. Data were collected from 142 Foundations in Portugal, which represents 50.9% of the Portuguese foundational sector. Supported by a Structural Equation Model (SEM), this study evaluates cause–effect relationship between Voluntary Disclosure, Board Structure, Organizations’ Characteristics and the existence of Auditing. Findings reveal that Organizations’ Characteristics influence the forms of Auditing, and on its turn, Auditing has a positive indirect impact on Voluntary Disclosure. Contrary to expectations, Board Structure does not affect Voluntary Disclosure. This paper fills a void in the literature by examining the impact of Foundations’ characteristics on their voluntary disclosure of financial and non-financial information.     

Maximum-Entropy Prior Uncertainty and Correlation of Statistical Economic Data

Empirical estimates of source statistical economic data such as trade flows, greenhouse gas emissions, or employment figures are always subject to uncertainty (stemming from measurement errors or confidentiality) but information concerning that uncertainty is often missing. This article uses concepts from Bayesian inference and the maximum entropy principle to estimate the prior probability distribution, uncertainty, and correlations of source data when such information is not explicitly provided. In the absence of additional information, an isolated datum is described by a truncated Gaussian distribution, and if an uncertainty estimate is missing, its prior equals the best guess. When the sum of a set of disaggregate data is constrained to match an aggregate datum, it is possible to determine the prior correlations among disaggregate data. If aggregate uncertainty is missing, all prior correlations are positive. If aggregate uncertainty is available, prior correlations can be either all positive, all negative, or a mix of both. An empirical example is presented, which reports relative uncertainties and correlation priors for the County Business Patterns database. In this example, relative uncertainties range from 1% to 80% and 20% of data pairs exhibit correlations below ?0.9 or above 0.9. Supplementary materials for this article are available online.     

An extended exponentiated-G-negative binomial family with threshold effect

In this paper, we formulate a very flexible family of models which unifies most recent lifetime distributions. The main idea is to obtain a cumulative distribution function to transform the baseline distribution with an activation mechanism characterized by a latent threshold variable. The new family has a strong biological interpretation from the competitive risks point of view and the Box–Cox transformation provides an elegant manner to interpret the effect on the baseline distribution to obtain this alternative model. Several structural properties of the new model are investigated. A Bayesian analysis using Markov Chain Monte Carlo procedure is developed to illustrate with a real data the usefulness of the proposed family.     

Haunting and the Aesthetics of Trauma in A Woman in Berlin and For Those Who Can Tell No Tales: Redefining Cultural Memory

ABSTRACT

This paper offers a comparative analysis of the aesthetics of trauma in the German film A Woman in Berlin and the Bosnian film For Those Who Can Tell No Tales, both of which address wartime rapes that happened in mid-to late twentieth century Europe. We use trauma studies as well as Avery Gordon's sociological theory of haunting to examine how two historical episodes of war-time rape (post-WWII Germany and late twentieth century Bosnia) have created ghostly effects that can be seen in the periodic return to these histories through artistic productions. Gordon argues that a traumatic past continues to co-exist with the present in the form of ghosts; thus, we need to produce case studies of haunting that attend to the social and political effects/affects of the ghostly and how they complicate our accounts of social trauma and oppression. We focus on: (1) the national and transnational reception of the two narratives; (2) the films’ similarities and differences regarding the cinematography of trauma, narrative styles used and female agency; and (3) masculinity and the male voices’ ‘right’ to tell a story. We conclude by addressing the limits of representation in both films and their role in challenging the dominant cultural memory.     

ASYMPTOTIC DISTRIBUTIONS OF SEASONAL UNIT ROOT TESTS: A UNIFYING APPROACH

This paper adopts a unified approach to the derivation of the asymptotic distributions of various seasonal unit root tests. The procedures considered are those of Dickey et al. [DHF], Kunst, Hylleberg et al. [HEGY], Osborn et al. [OCSB], Ghysels et al. [GHL] and Franses. This unified approach shows that the asymptotic distributions of all these test statistics are functions of the same vector of Brownian motions. The Kunst test and the overall HEGY F-test are, indeed, equivalent both asymptotically and in finite samples, while the Franses and GHL tests are shown to have equivalent parameterizations. The OCSB and DHF test regressions are viewed as restricted forms of the Kunst-HEGY regressions, and these restrictions may have non-trivial asymptotic implications.     

Inference for the software reliability using asymmetric los fimctions:a hierarchical bayes approach

The aim of this paper is to propose a hierarchical Bayes approach and an appropriate loss function to perform a Bayesian analysis of the total number of software failures denoted by N. It is shown that the Bayes procedure is more stable than the maximum likelihood procedure and a stopping rule for debugging the software is suggested via the LIN EX loss function.     

Randomized singular spectrum analysis for long time series

ABSTRACT

Singular spectrum analysis (SSA) is a relatively new method for time series analysis and comes as a non-parametric alternative to the classical methods. This methodology has proven to be effective in analysing non-stationary and complex time series since it is a non-parametric method and do not require the classical assumptions over the stationarity or over the normality of the residuals. Although SSA have proved to provide advantages over traditional methods, the challenges that arise when long time series are considered, make the standard SSA very demanding computationally and often not suitable. In this paper we propose the randomized SSA which is an alternative to SSA for long time series without losing the quality of the analysis. The SSA and the randomized SSA are compared in terms of quality of the model fit and forecasting, and computational time. This is done by using Monte Carlo simulations and real data about the daily prices of five of the major world commodities.