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This chapter proposes an approach toward the estimation of cross-sectional sample selection models, where the shocks on the units of observation feature some interdependence through spatial or network autocorrelation. In particular, this chapter improves on prior Bayesian work on this subject by proposing a modified approach toward sampling the multivariate-truncated, cross-sectionally dependent latent variable of the selection equation. This chapter outlines the model and implementation approach and provides simulation results documenting the better performance of the proposed approach relative to existing ones.

The Supreme Court's recent cross burning case – Virginia v. Black (2003) – saw dueling historical narratives. Justice O’Connor, writing for the majority, painted a history in which the Klan often burned crosses to intimidate, but also did so for other, “expressive” reasons. Justice Thomas, in dissent, related a history in which the burning cross never speaks. Interestingly, O’Connor and Thomas used many of the same historical sources. How did they reach such different results? While both O’Connor and Thomas interpreted (and stretched) the historical sources in different directions, their dispute ultimately turned on their diverging doctrinal views.

It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold autoregression with GARCH errors, TGARCH, and double threshold heteroskedastic model with auxiliary threshold variables. Further, we briefly review recent methods for Bayesian model selection, such as, reversible-jump Markov chain Monte Carlo, Monte Carlo estimation via independent sampling from each model, and importance sampling methods. Seven heteroskedastic models are then compared, for three long series of daily Asian market returns, in a model selection study illustrating the preferred model selection method. Major evidence of nonlinearity in mean and volatility is found, with the preferred model having a weighted threshold variable of local and international market news.

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

Public agencies entrusted with fire management in the western U.S. are faced with a decision each time a fire starts: should it be suppressed, or should it be left to burn? In some cases, fires that have not been rapidly staffed and suppressed have later proved very expensive and dangerous to suppress; and in other cases, fires that would never have caused large impacts are suppressed, missing an opportunity to reduce fuel loading and to cycle nutrients. In this chapter, the command structure through which these decisions are made is reviewed in basic terms, and a description is provided of how a fire goes from initial detection to being staffed by firefighters involved in fire suppression. Initial attack resources are discussed with an emphasis on the aerially delivered firefighters who often are responsible for suppressing remote fires. Finally, opportunities to improve the process of making fire suppression decisions are explored, and potential decision-support systems integrating firefighter knowledge with emerging technologies are discussed.

In this paper, I propose an algorithm combining adaptive sampling and Reversible Jump MCMC to deal with the problem of variable selection in time-varying linear model. These types of model arise naturally in financial application as illustrated by a motivational example. The methodology proposed here, dubbed adaptive reversible jump variable selection, differs from typical approaches by avoiding estimation of the factors and the difficulties stemming from the presence of the documented single factor bias. Illustrated by several simulated examples, the algorithm is shown to select the appropriate variables among a large set of candidates.

Missing data are a pervasive problem in special education research. The purpose of this chapter is to provide researchers with an overview of two “modern” alternatives for handling missing data, full information maximum likelihood (FIML) and multiple imputation (MI). These techniques are currently considered to be the methodological “state of the art”, and generally provide more accurate parameter estimates than the traditional methods that are still common in published educational studies. The chapter begins with an overview of missing data theory, and provides brief descriptions of some traditional missing data techniques and their requisite assumptions. Detailed descriptions of FIML and MI are given, and the chapter concludes with an analytic example from a longitudinal study of depression.

A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outcome probabilities that enter the likelihood function. Calculation of these probabilities involves high-dimensional integration, making simulation methods indispensable in both Bayesian and frequentist estimation and model choice. We review several existing probability estimators and then show that a broader perspective on the simulation problem can be afforded by interpreting the outcome probabilities through Bayes’ theorem, leading to the recognition that estimation can alternatively be handled by methods for marginal likelihood computation based on the output of Markov chain Monte Carlo (MCMC) algorithms. These techniques offer stand-alone approaches to simulated likelihood estimation but can also be integrated with traditional estimators. Building on both branches in the literature, we develop new methods for estimating response probabilities and propose an adaptive sampler for producing high-quality draws from multivariate truncated normal distributions. A simulation study illustrates the practical benefits and costs associated with each approach. The methods are employed to estimate the likelihood function of a correlated random effects panel data model of women's labor force participation.

Purpose – The purpose of this study is to reveal a missing chapter of Australian Aboriginal history. Jack Johnson is known as the first black Heavyweight Champion of the world but little is known of his inspiration to many oppressed groups around the globe including Aboriginal Australia. Johnson was flamboyant, outspoken and deeply proud of his heritage.

Design/methodology/approach – This chapter is undertaken as restorative history and examines the interconnected international networks of cultural exchange operational in the early decades of the twentieth century. It privileges the tools of historical narrative (story) as a major method, and is based largely on historical newspapers sources’. Press coverage can provide fascinating insight into historical characters and can deliver their voice and thoughts at the time, and newspapers remain important in forming public opinion.

Findings – Jack Johnson would become one of many influences from the international Black Diaspora upon Aboriginal Australia across the twentieth century.

Originality/value – John Maynard’s work on Jack Johnson (Maynard, J. (2003). Vision, Voice and Influence – The rise of the Australian Aboriginal Progressive Association. Australian Historical Studies, 121(April), 91–105, 2005, 2007) and more recently Theresa Runstedtler’s study (2012) has uncovered transnational links of Jack Johnson to many oppressed groups globally including Aboriginal Australia. This current study places Johnson’s impact upon Aboriginal Australia at the forefront of a shift and awakening awareness of Aboriginal Australians of their global political and racial challenges.