Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

OLS and IV estimation of regression models including endogenous interaction terms

We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show how to generate instrumental variables using the nonlinear functional form of the structural equation when traditional excluded instruments are unknown. We propose to use these instruments with identification robust IV inference. We furthermore show that, whenever functional form identification is not valid, the ordinary least squares (OLS) estimator of the coefficient of the interaction term is consistent and standard OLS inference applies. Using our alternative empirical methods we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth.     

A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.     

Extending Bayesian back-calculation to estimate age and time specific HIV incidence

Lifetime Data Analysis - CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their...     

Twenty Years of Economics Curriculum: Trends,Issues, and Transformations?

This is a comprehensive review of literature related to K-12 economics curriculum spanning 20 years, from 1998-2018. The search for literature revealed 18 articles and book chapters in peer-reviewed journals and volumes related to social studies and economics education. The study revealed several trends and issues related to K-12 economics textbooks, including publishing trends, the extent to which economics curriculum is relevant and enactive, and the influence of neoclassical economic theory in serving as the “official knowledge” undergirding the curriculum. The review presents debates in the field related to these issues. The study also reveals the possible influence of the 2008 financial crisis, revealing that economics curriculum seems to have changed little, although the number of scholars critiquing the curriculum has risen dramatically.     

SODA WARS: THE EFFECT OF A SODA TAX ELECTION ON UNIVERSITY BEVERAGE SALES

We examine how soda sales changed due to the campaign attention and election outcome of a local excise tax on sugar‐sweetened beverages. Using panel data of beverage sales from university retailers in Berkeley, California, we estimate that soda purchases relative to control beverages significantly dropped immediately after the election, months before the tax was implemented in the city of Berkeley or on campus. Supplemental scanner data from off‐campus retailers reveal this result is not unique to the university setting. Our findings suggest soda tax media coverage and election outcomes can have larger effects on purchasing behavior than the tax itself. (JEL D12, H20, C23, I38, Q18)     

Trajectories of Interracial Heterosexual Couples: A Longitudinal Analysis of Relationship Quality and Separation

Minimal research has examined interracial couples’ relationship satisfaction and dissolution patterns over time. Using dyadic data across 8 years, we examined potential differences in relationship quality trajectories through latent growth curve modeling with 1,336 couples as well as differential risks for relationship dissolution through logistic regression between same‐race and interracial relationships using a different subset of 2,370 couples. Results indicated that women in interracial relationships were significantly lower in initial relationship quality than women in same‐race relationships. Further, interracial couples of Black‐Hispanic partnerings were twice as likely to separate across 8 years than same‐race couples. These results suggest that despite interracial relationships not substantively differing from same‐race couples in trajectories of relationship quality, specific Black‐Hispanic interracial couples are at a higher risk of eventual separation.     

Establishing Safety in Emotionally Focused Couple Therapy: A Single‐Case Process Study

Emotionally Focused Couple Therapy (EFT) is an effective approach to working with distressed couples. It is not only effective in reducing relationship distress, but also in successfully maintaining gains over time. In the current study, we sought to understand the therapy processes that result in the creation of safety during stage one of EFT. Safety is a key ingredient in stage one, setting the stage for deeper interpersonal and intrapsychic work in later stages of treatment. In order to achieve the study aim, we analyzed video recordings of the first six sessions of a single couple case from an EFT randomized controlled trial. Using a theory building case study approach, results reveal that all expected components related to the creation of safety were achieved. Additional findings from our analyses indicate that the therapist was highly active, especially in seizing key moments in the therapy provided by this particular case, which shifted the couple in the direction of safety and deescalated their interactional cycle. Implications of key findings are discussed.     

Life Balance and Work Addiction Among NCAA Administrators and Coaches

Intercollegiate athletics represent an industry prone to challenges of work addiction and life balance. Work addiction and life balance domains in male and female intercollegiate athletic coaches and administrators (N = 245) were examined using multiple regression analysis. Higher levels of stress/anxiety, sleep disturbance, and career satisfaction and lower levels of quality of relationships and friendships were the strongest predictors of work addiction. Counseling focused on reducing the negative aspects of work addiction, while maintaining job performance, may benefit this population.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.