Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.     

Investigating impacts of complex sampling on latent growth curve modelling

We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.     

Employment status of women with disabilities from the Behavioral Risk Factor Surveillance Survey (1995-2002)

Among working aged adults (18-64) with disabilities, three out of 10 (32%) work full or part-time, compared to eight out of 10 (81%) of those without disabilities [9]. In addition, 24.7% of women with a severe disability and 27.8% of men with a severe disability are employed, while women with a non-severe disability have an employment rate of 68.4% and men with a non-severe disability have an employment rate of 85.1% [14]. This study examined data from the Behavioral Risk Factor Surveillance Survey from 1995-2002 to determine whether or not disparities exist in the rate of unemployment for women with disabilities, compared to men with disabilities and women and men without disabilities. In addition, regression analysis looked at the how disability and gender predict the outcome of unemployment. Results showed that there has been essentially no change with regard to employment for any of these populations. In addition, disability and gender were found to be the strongest predictors of unemployment for women with disabilities. Possible explanations were discussed as to the reasons for the results and issues were presented for future research.     

Older women's fears of violence: the need for interventions that enable active ageing

Women's fear of violence can impact negatively on their active participation in life. An ageing survey conducted with 2,620 Australian respondents aged 50 to 90 years examined aspects of work, learning, social, spiritual and emotional status, health, vision, home, life events, demographics, and asked an open-ended question about what being actively engaged in life meant. Ordinal regression was carried out on two dependent variables: wanting and needing to learn to discourage violence. Analyses found that as women's age increased, those on lower incomes were more likely than others to say they needed to learn how to discourage violence against them. This paper investigates the variables associated with the findings-transport, finances, news media, home safety, and reduced social interactions. Results highlight the importance of understanding women's fear in the context of personal and social issues, and the need to provide learning opportunities to improve safety and social engagement.     

Gay, lesbian, bisexual and transgendered (GLBT) experiences with Earth-spirited faith

Earth-spirited faiths (e.g., Pagan and Wiccan spiritualities) have been described as more affirmative toward gay, lesbian, bisexual, and transgendered (GLBT) members than mainstream Judeo-Christian faiths, but no research has explored Earth-spirited faiths' GLBT-affirming behaviors. This study investigated those behaviors as well as the faith experiences of GLBT Earth-spirited individuals. At time of coming out, participants who were affiliated with mainstream Judeo-Christian faiths reported greater faith conflict than those affiliated with Earth-spirited faiths; however, there were no differences in resolution of the conflict between the two groups, internalized homonegativity, or self-acceptance. In addition, Earth-spirited faiths engaged in many GLBT-affirming behaviors.     

Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.     

Confidence intervals for secondary parameters following a sequential test

In sequential studies, formal interim analyses are usually restricted to a consideration of a single null hypothesis concerning a single parameter of interest. Valid frequentist methods of hypothesis testing and of point and interval estimation for the primary parameter have already been devised for use at the end of such a study. However, the completed data set may warrant a more detailed analysis, involving the estimation of parameters corresponding to effects that were not used to determine when to stop, and yet correlated with those that were. This paper describes methods for setting confidence intervals for secondary parameters in a way which provides the correct coverage probability in repeated frequentist realizations of the sequential design used. The method assumes that information accumulates on the primary and secondary parameters at proportional rates. This requirement will be valid in many potential applications, but only in limited situations in survival analysis.     

The Social Bases of Rural‐Urban Political Divides: Social Status,Work, and Sociocultural Beliefs

The rural‐urban political divide has sparked media and social science concern. Yet national studies of rural and urban voters have largely failed to draw from the distinct conceptual literatures produced by rural sociologists. We take a new look at individuals’ voting choices, building from two rural sociological literatures, research on spatial inequality and on the rural‐urban continuum, to identify the social bases anteceding Republican voting in presidential elections. We analyze three social bases along which rural‐urban populations vary: social structural statuses, work and employment, and sociocultural values and beliefs. We question the degree to which rural‐urban differences can be accounted for by these factors. Data are from approximately 9,000 respondents to the General Social Surveys for election years 2000–2012. Our findings demonstrate that the literatures produced by rural sociologists provide a strong conceptual foundation for explaining rural‐urban voting differences. Rural and urban residents’ differential social statuses account for the greatest variation in their voting choices. Sociocultural values and beliefs, particularly attitudes toward domestic social issues, are also important. Findings add significant insight into the variety of factors that differentiate rural‐urban individuals’ voting choices as well as illuminate the need for greater emphasis on exurban voters.     

Graphical representation of survival curves in the presence of time-dependent categorical covariates with application to liver transplantation

Graphical representation of survival curves is often used to illustrate associations between exposures and time-to-event outcomes. However, when exposures are time-dependent, calculation of survival probabilities is not straightforward. Our aim was to develop a method to estimate time-dependent survival probabilities and represent them graphically. Cox models with time-dependent indicators to represent state changes were fitted, and survival probabilities were plotted using pre-specified times of state changes. Time-varying hazard ratios for the state change were also explored. The method was applied to data from the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL). Survival curves showing a ‘split’ at a pre-specified time t allow for the qualitative comparison of survival probabilities between patients with similar baseline covariates who do and do not experience a state change at time t. Time since state change interactions can be visually represented to reflect changing hazard ratios over time. A2ALL study results showed differences in survival probabilities among those who did not receive a transplant, received a living donor transplant, and received a deceased donor transplant. These graphical representations of survival curves with time-dependent indicators improve upon previous methods and allow for clinically meaningful interpretation.