Forgiving Perpetrators of Violence: Colombian People’s Positions

Colombian people’s positions regarding the granting of forgiveness to persons who have been more or less actively involved in the violence that ravaged the country during the past 60 years were examined. Four hundred lay people living in Bogota were presented with 48 concrete cases in which a former perpetrator of violence (a member of the guerillas, the paramilitary, the military or a drug cartel) asked for forgiveness from a victim’s family. These cases were constructed using a three-factor orthogonal design: Degree of Responsibility × Severity of the Negative Acts Committed × Apologies. Four basic positions were found. The most common one, which was shared by nearly 40 % of the sample, mostly people from the wealthier segments of society, was “no forgiveness under any condition”. Eighteen percent of the participants, mostly from the poorest segments of society, considered that forgiveness could be granted each time the former perpetrators expressed true repentance (and, in the case of former organizers, if they have offered adequate compensation and had not committed very severe crimes). This was the most frequently observed attitude when perpetrators were former members of the paramilitary. Fifteen percent of the participants considered that forgiveness should be systematically granted. Finally, 28 % of the participants were undecided about the issue.     

Linear censored regression models with skew scale mixtures of normal distributions

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.     

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.