Bayesian networks for imputation

Summary.  Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables ( statistical consistency ) and preservation of logical constraints in data ( logical consistency ). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.     

Will taxpayers ever like taxes?: Responses to the U.S. Tax Reform Act of 1986

Major policy changes like the 1986 Tax Reform Act (TRA) in the United States provide natural experiments to study attitudinal and behavior responses to law. Surprisingly, public evaluations of the TRA became increasingly negative after passage, while general support for the tax system became more positive in response to the changes. To explain this puzzle, we propose a dynamic model of taxpayer attitudes that is consistent with the observed loose linkage between logically-connected concepts. We briefly review the political context, objectives and impacts of the 1986 Tax Reform Act, and present several studies testing the impact of self-interest, prior beliefs, and social discussion on changes in attitudes and behavior.Results suggest that the complexity of taxes and difficulty in knowing objective impacts weakens the role of self-interest in attitudinal changes, thereby strengthening the role of prior beliefs and discussion. Evaluations of tax laws are most responsive to more politically-oriented influences.     

Firm-level training in local economic systems: Complementarities in production and firm innovation strategies

The paper presents a conceptual review over the main aspects concerning the role of human capital investments and training activities within production processes, followed by empirical evidence on two local economic systems in Northern Italy, based on recent survey data. Theoretical and empirical considerations are brought together in order to provide new insights on the role of training and the factors associated to training activities at firm level.The potential driving factors of training here analysed compounds structural characteristics, labour demand dynamics, human resource management practices, workforce features, and firm performances. We observe that training activities emerge positively associated with high-performance practices, innovative labour demand features, workforce skill level, firm size, and are affected by labour flexibility in various directions. Empirical evidence confirms most previous results but also adds further important insights. The analysis suggests that a widening gap, between few innovatively evolving and many stagnant firms, could characterise the future dynamics of the Region. The high relevance of structural variables, labour demand factors and HRM/innovation practices shows that regional industrial policies and labour policies should be jointly implemented for increasing potential firm productivity. This is a key concern for the current debate on local systems economic development in the European and Italian environment.     

Higher education outcomes, graduate employment and university performance indicators

Summary.  Official employment-related performance indicators in UK higher education are based on the population of students responding to the 'First destination supplement' (FDS). This generates potentially biased performance indicators as this population of students is not necessarily representative of the full population of leavers from each institution. University leavers who do not obtain qualifications and those who do not respond to the FDS are not included within the official analysis. We compare an employment-related performance indicator based on those students who responded to the FDS with alternative approaches which address the potential non-random nature of this subgroup of university leavers.     

Estimating rating transition probabilites with missing data

In this article we provide a rigorous treatment of one of the central statistical issues of credit risk management. GivenK-1 rating categories, the rating of a corporate bond over a certain horizon may either stay the same or change to one of the remainingK-2 categories; in addition, it is usually the case that the rating of some bonds is withdrawn during the time interval considered in the analysis. When estimating transition probabilities, we have thus to consider aK-th category, called withdrawal, which contains (partially) missing data. We show how maximum likelihood estimation can be performed in this setup; whereas in discrete time our solution gives rigorous support to a solution often used in applications, in continuous time the maximum likelihood estimator of the transition matrix computed by means of the EM algorithm represents a significant improvement over existing methods.     

Ranking Nuclear and Radiological Terrorism Scenarios: The Italian Case

A quantitative criterion for ranking the different scenarios of nuclear and radiological terrorism has been developed. The aim of the model is not to predict terroristic events but only to indicate which scenario has the higher utility from the point of view of a terroristic organization in terms of balance between factors favoring and discouraging the attack, respectively. All these factors were quantified according to a scoring system that takes into account the logarithmic relationship between perceptions and stimuli. The criterion was applied to several scenarios, each of which was modeled in a simple but not trivial way in order to estimate the expected damage in terms of probable life losses from both radiative and nonradiative effects. The outcome from the ranking method indicates that the attractive scenario appears to be the detonation of a low yield improvised nuclear device in the metropolitan area of a major city.     

Bayesian analysis of elapsed times in continuous‐time Markov chains

The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.