Structured Voluntary Youth Activities and Positive Outcomes in Adulthood: An Exploratory Study of Involvement in Scouting and Subjective Well-Being

This study explores whether youth involvement in Scouting has positive consequences later in life. We examine whether the number of years of participation in Scouting is positively associated with human and social capital and recreational lifestyles in adulthood, and whether these are linked to subjective well-being: relational, emotional, and physical health. To explore this potential relationship, we estimated a structural equation model, analyzing data from a national sample of adult males. We found that youth involvement in Scouting is positively related to subjective well-being indirectly via the positive adult outcomes.     

PROGRAM AND TRAINEE LIFESTYLE STRESS: A SURVEY OF AAMFT STUDENT MEMBERS

Student members of the American Association for Marriage and Family Therapy (n = 900) were randomly selected and surveyed regarding perceptions of certain MFT training program and graduate student lifestyle stressors. Teh return rate was 329 (37%) of the 900 students surveyed. The sample was 70% female and 30% male. Sixty-five percent of the sample were married. Sixty-one percent were in master's, 25% were in doctral, and 12% were in other postgraduate programs. Potential high stressors were the number of hours students worked to support themselves and the number of hours spouses or significant others worked. Twenty-seven percent of students had considered dropping out due to program demands. Only 11.2% had actually done so. Nearly 11% reported current antidepressant use. Recommendations for further research directions on the topic of MFT trainee stress are provided     

Toward a standard of care for child ADHD: implications for marriage and family therapists

In this article, we briefly review the etiology of Attention Deficit Hyperactivity Disorder (ADHD) and describe the most current scientifically supported theory about the nature of this disorder. Based on the research literature, several organizations have established standards of care for its diagnosis and treatment, which involves a multimodal strategy for treatment at the biological, individual, family, school, and community levels. Assessment and treatment guidelines are provided for Marriage and Family Therapists (MFTs), and specific areas of competency are proposed for MFTs who are working with children who have ADHD.     

Selection-Based Versus Topography-Based Responding: An Important Distinction for Stimulus Equivalence?

The Analysis of Verbal Behavior - English-speaking subjects were taught 16 English-French word pairs. Within any given trial, one word from each pair was presented; for eight items, subjects were...     

Particle Learning for Fat-Tailed Distributions

It is well known that parameter estimates and forecasts are sensitive to assumptions about the tail behavior of the error distribution. In this article, we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a t_ν-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empirical and theoretical analysis of the rate of learning of tail thickness under a default Jeffreys prior. We illustrate our sequential methodology on the British pound/U.S. dollar daily exchange rate data and on data from the 2008–2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.     

Inference for nonconjugate Bayesian Models using the Gibbs sampler

A Bayesian approach to modeling a rich class of nonconjugate problems is presented. An adaptive Monte Carlo integration technique known as the Gibbs sampler is proposed as a mechanism for implementing a conceptually and computationally simple solution in such a framework. The result is a general strategy for obtaining marginal posterior densities under changing specification of the model error densities and related prior densities. We illustrate the approach in a nonlinear regression setting, comparing the merits of three candidate error distributions.     

Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970–1996

In this article we present a technique for implementing large-scale optimal portfolio selection. We use high-frequency daily data to capture valuable statistical information in asset returns. We describe several statistical issues involved in quantitative approaches to portfolio selection. Our methodology applies to large-scale portfolio-selection problems in which the number of possible holdings is large relative to the estimation period provided by historical data. We illustrate our approach on an equity database that consists of stocks from the Standard and Poor's index, and we compare our portfolios to this benchmark index. Our methodology differs from the usual quadratic programming approach to portfolio selection in three ways: (1) We employ informative priors on the expected returns and variance-covariance matrices, (2) we use daily data for estimation purposes, with upper and lower holding limits for individual securities, and (3) we use a dynamic asset-allocation approach that is based on reestimating and then rebalancing the portfolio weights on a prespecified time window. The key inputs to the optimization process are the predictive distributions of expected returns and the predictive variance-covariance matrix. We describe the statistical issues involved in modeling these inputs for high-dimensional portfolio problems in which our data frequency is daily. In our application, we find that our optimal portfolio outperforms the underlying benchmark.     

Bayesian Instrumental Variables: Priors and Likelihoods

Instrumental variable (IV) regression provides a number of statistical challenges due to the shape of the likelihood. We review the main Bayesian literature on instrumental variables and highlight these pathologies. We discuss Jeffreys priors, the connection to the errors-in-the-variables problems and more general error distributions. We propose, as an alternative to the inverted Wishart prior, a new Cholesky-based prior for the covariance matrix of the errors in IV regressions. We argue that this prior is more flexible and more robust thanthe inverted Wishart prior since it is not based on only one tightness parameter and therefore can be more informative about certain components of the covariance matrix and less informative about others. We show how prior-posterior inference can be formulated in a Gibbs sampler and compare its performance in the weak instruments case for synthetic as well as two illustrations based on well-known real data.     

Bayesian Analysis of Stochastic Volatility Models

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of our Markov-chain method. In addition, multistep-ahead predictive densities can be constructed that reflect both inherent model variability and parameter uncertainty. We illustrate our method by analyzing both daily and weekly data on stock returns and exchange rates. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches.