Missing data and forecasting in multivariate time series: An application of the common components dynamic linear model

Summary The paper deals with missing data and forecasting problems in multivariate time series making use of the Common Components Dynamic Linear Model (DLMCC), presented in Quintana (1985), and West and Harrison (1989). Some results are presented and discussed: exploiting the correlation between series, estimated by the DLMCC, the paper shows as it is possible to update state vector posterior distributions for the unobserved series. This is realized on the base of the updating of the observed series state vectors, for which the usual Kalman filter equations can be applied. An application concerning some Italian private consumption series provides an example of the model capabilities.     

The evaluation of DNA evidence in pedigrees requiring population inference

Summary. The evaluation of nuclear DNA evidence for identification purposes is performed here taking account of the uncertainty about population parameters. Graphical models are used to detail the hypotheses being debated in a trial with the aim of obtaining a directed acyclic graph. Graphs also clarify the set of evidence that contributes to population inferences and they also describe the conditional independence structure of DNA evidence. Numerical illustrations are provided by re-examining three case-studies taken from the literature. Our calculations of the weight of evidence differ from those given by the authors of case-studies in that they reveal more conservative values.     

The disenchantment of Bitcoin: unveiling the myth of a digital currency

Bitcoin and its peculiar, decentralized transaction system, have already ignited interest by professional and retail traders in search for profits and by economists and legal experts, looking for possible regulation to contain illegal uses. We instead examine the unexpected and ongoing success of Bitcoin from a sociological perspective, first questioning its unusual legitimation system, backed by the so called ‘blockchain technology’, instead of by governmental authorities. Then we collect data and elements to reconstruct Bitcoin’s history as a cryptocurrency, starting from the mysterious story surrounding its birth. We then follow its spread and development through social networks and words of mouth, together with its sudden booms and bursts, finally to suggest that both users and institutional regulators should be aware of the risks of Bitcoin and of its alleged power to challenge our very notion of money.     

Social networks of knowledge production in Brazilian psychology: comparing sub-areas

The social processes underlying the production of scientific knowledge make it an inherently social endeavor. The interaction between systems of thought and affiliation has been subject of the sociology of science, with remarkable developments in studies on the structure of scientific fields based on social networks analysis. Although psychology is one of the pioneer fields investigated in this literature, there is scarcity of research on psychology. The objective of this paper is to describe the social networks’ dynamics of knowledge production in psychology in Brazil, comparing association patterns among its sub-areas. For this purpose, 395 researchers responded an electronic questionnaire about their relationships. Networks of nine psychology sub-areas were analyzed for structural properties (i.e. density, centrality) and relational exchanges (the connecting roles of boundary spanners and central connectors). Findings showed different structures and connectivity patterns between networks, but most of these areas configure as small worlds. Discussion explores implications of these findings for the production of knowledge within areas, and for the national production of psychological knowledge as a whole, with effects on the international projection of knowledge produced in Brazil.     

Learning the two parameters of the Poisson–Dirichlet distribution with a forensic application

In forensic science, the rare type match problem arises when the matching characteristic from the suspect and the crime scene is not in the reference database; hence, it is difficult to evaluate the likelihood ratio that compares the defense and prosecution hypotheses. A recent solution consists of modeling the ordered population probabilities according to the two-parameter Poisson–Dirichlet distribution, which is a well-known Bayesian nonparametric prior, and plugging the maximum likelihood estimates of the parameters into the likelihood ratio. We demonstrate that this approximation produces a systematic bias that fully Bayesian inference avoids. Motivated by this forensic application, we consider the need to learn the posterior distribution of the parameters that governs the two-parameter Poisson–Dirichlet using two sampling methods: Markov Chain Monte Carlo and approximate Bayesian computation. These methods are evaluated in terms of accuracy and efficiency. Finally, we compare the likelihood ratio that is obtained by our proposal with the existing solution using a database of Y-chromosome haplotypes.     

When money does not buy happiness: The case of “frustrated achievers”

An increase in real per capita income is generally expected to be associated with nonnegative variations in life satisfaction. The alternative (association with negative changes) is generally defined as “frustrated achievement” [Graham, C., Pettinato, S., 2002. Happiness and Hardship: Opportunity and Insecurity in New Market Economies. The Brookings Institution Press, Washington, D.C.].We investigate the determinants of “frustrated achievement” in the German socioeconomic panel on more than 30,000 individuals collected between 1992 and 2004. We observe a parallel reduction in self-declared life satisfaction corresponding to almost one-third of yearly increases in (equalised) real household income.Our econometric findings show that the lack of a full-time job, health deterioration, relative income effects, marital status shocks and poorer social life are the main factors associated with this phenomenon.     

Public corruption: A study across regions in Italy

This work explores the determinants of public corruption using a regional panel dataset on crimes perpetrated in Italy by public officials against the public administration in combination with a set of demographic and socio-economic variables. The results suggest that both the size and the composition of public spending at the local level explain corruption. We also find that regions where social capital is higher are more likely to face a lower incidence of corruption crimes. Moreover, regions which have historically placed less importance on rooting out corruption may be stuck in a vicious circle of higher levels of corruption.