Estimating the Structure of Agricultural Investment: A Stochastic- Coefficients Approach

Two approaches, a flexibie-accelerator model and a stochastic-coefficients alternative, are used to estimate the structure of aggregate agricultural investment. Structural estimates of the adjustment rates for each model differ. The stochastic-coefficients model, however, performs better in out-of-sample forecasting.     

A Survey of the Teaching of Statistics in M.B.A. Programs

A survey of business schools was conducted to obtain information about the current state of the teaching of business statistics to students enrolled in M.B.A. degree programs. The survey was undertaken for and presented at a June 1986 conference on “Making Statistics More Effective in Schools of Business,” held at the University of Chicago's Graduate School of Business. Information was elicited concerning both the required statistics sequence and elective statistics courses for M.B.A. students, as well as computer usage in these courses. This article summarizes the information obtained from the survey.     

Web of Deception: Misinformation on the Internet

Abstract

“Serials Spoken Here” has reports on a symposium on “The Transition to Open Access Scholarship” held at the University at Albany, New York, and the 2004 North Carolina Serials Conference, both held in April 2004.     

Reviews

Abstract

Elizabeth Parang reviews Reengineering the Library: Issues in Electronic Resources Management. Scott Johnson reviews Academic Library Management: Case Studies.     

Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models

ABSTRACT

In panel data models and other regressions with unobserved effects, fixed effects estimation is often paired with cluster-robust variance estimation (CRVE) to account for heteroscedasticity and un-modeled dependence among the errors. Although asymptotically consistent, CRVE can be biased downward when the number of clusters is small, leading to hypothesis tests with rejection rates that are too high. More accurate tests can be constructed using bias-reduced linearization (BRL), which corrects the CRVE based on a working model, in conjunction with a Satterthwaite approximation for t-tests. We propose a generalization of BRL that can be applied in models with arbitrary sets of fixed effects, where the original BRL method is undefined, and describe how to apply the method when the regression is estimated after absorbing the fixed effects. We also propose a small-sample test for multiple-parameter hypotheses, which generalizes the Satterthwaite approximation for t-tests. In simulations covering a wide range of scenarios, we find that the conventional cluster-robust Wald test can severely over-reject while the proposed small-sample test maintains Type I error close to nominal levels. The proposed methods are implemented in an R package called clubSandwich. This article has online supplementary materials.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.     

Family environments and the relationship context of first adolescent sex: Correlates of first sex in a casual versus steady relationship

Limited research has examined how family environments are associated with the relationship context of first sex, an important indicator of reproductive health risk. We use data from the 1997 National Longitudinal Survey of Youth to assess the association between the family environment - parent-parent relationships, parent-adolescent relationships, and family structure - and relationship context of first heterosexual sexual intercourse, distinguishing between the transition to first sex in serious and casual relationships. Twenty-five percent of females and 43% of males who had sex by age 18 did so in a casual relationship. All dimensions of the family environment were linked to the relationship context of first sex. Notably, higher parental monitoring was associated with a reduced risk of transitioning to first sex in a casual relationship versus no sex, and greater family routines were associated with a reduced risk of transitioning to sex in a steady relationship versus having no sex, for males and females. A strong maternal-adolescent relationship was associated with a reduced risk of first sex in a casual relationship but only for males. Additionally, in two-parent families, a strong father-adolescent relationship was associated with reduced risk of transitioning to casual sex, but only for females. Pregnancy and STI prevention programs should work with parents to foster positive parent-adolescent relationships, to become aware of their adolescents’ activities and to recognize that parents are important models for adolescent relationship behaviors.