A Dynamic Model of Demand for Houses and Neighborhoods

This paper develops a dynamic model of neighborhood choice along with a computationally light multi‐step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non‐marketed amenities—neighborhood air pollution, violent crime, and racial composition—in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.     

The Effects of Multitasking on Operations Scheduling

This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.     

Optimal Digital Rights Management with Uncertain Piracy

Many firms that sell digital copies of copyrighted materials online face a common dilemma: the use of digital rights management (DRM) to impede pirates can impose restrictions on legitimate use. We introduce a two‐period model in which the use of DRM in the first period affects the probability that a consumer finds a pirated copy in the second period; the threat of legal action reduces consumers’ consumption of pirated copies; and firms choose whether to sell, and at what prices, either strongly or weakly DRM‐protected products, or both. Furthermore, we incorporate the role of uncertainty concerning future levels of piracy. Using a two‐period model with uncertainty, we investigate a firm's optimal DRM strategies and present the optimal pricing strategy as well as product launch strategy under different market conditions. We find that one important characteristic of the optimal strategy is that it is optimal to maintain the same product line configuration strategy for both periods. We also characterize the conditions under which each strategy is optimal.     

A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.     

A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors

This article considers a nonparametric additive seemingly unrelated regression model with autoregressive errors, and develops estimation and inference procedures for this model. Our proposed method first estimates the unknown functions by combining polynomial spline series approximations with least squares, and then uses the fitted residuals together with the smoothly clipped absolute deviation (SCAD) penalty to identify the error structure and estimate the unknown autoregressive coefficients. Based on the polynomial spline series estimator and the fitted error structure, a two-stage local polynomial improved estimator for the unknown functions of the mean is further developed. Our procedure applies a prewhitening transformation of the dependent variable, and also takes into account the contemporaneous correlations across equations. We show that the resulting estimator possesses an oracle property, and is asymptotically more efficient than estimators that neglect the autocorrelation and/or contemporaneous correlations of errors. We investigate the small sample properties of the proposed procedure in a simulation study.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

Matching in sexual experience for married, cohabitating, and dating couples

The present study examined heterosexual romantic partners' number of intercourse partners prior to the initiation of their relationship to determine if a significant positive correlation (matching) occurred between partners, and if this matching was associated with their level of love, satisfaction with, and commitment to the relationship. One hundred and six couples who were dating, cohabitating, or married participated in this study. Results indicated that, with the exception of cohabitating couples, romantic partners showed a significant level of matching in the prior number of intercourse partners. Further, among the married couples, a higher discrepancy between men's and women's number of previous intercourse partners was related to lower levels of love, satisfaction, and commitment in the relationship.     

Differences by gender, ethnicity, and acculturation in the efficacy of the keepin' it REAL model prevention program

This study examined whether the efficacy of keepin' it REAL, a model program for substance use prevention in schools, was moderated by gender, ethnicity, and acculturation. Gender differences in program efficacy may arise through boys' higher risk of drug use, inadequate attention to girls' developmental issues, or cultural factors like polarized gender expectations. Data came from a randomized trial in 35 Phoenix, Arizona, middle schools involving 4,622 mostly Latino 7th graders. Using multi-level mixed models and multiple imputation missing techniques, results for the total sample showed no gender differences in program effects on recent substance use, but the program was more effective in fostering boys' than girls' anti-drug norms. Subgroup analyses demonstrated several more beneficial program effects for boys than girls (less alcohol and cigarette use and stronger anti-drug norms), but only among less acculturated Latinos. There were no gender differences in program effects among more acculturated Latinos, nor among non-Latino whites.     

Gambling Behavior of Student-Athletes and a Student Cohort: What are the Odds?

This study investigated the prevalence of gambling, gambling related NCAA violations, and disordered gambling in student-athletes (n = 736) with a comparison cohort of students (n = 1,071) at four universities. Student-athletes reported similar rates of gambling frequency, use of a bookmaker, and disordered gambling as students. After accounting for demographic differences, student-athletes were less likely to engage in sports wagering than students. Several risk factors for disordered gambling were identified, including being male and reporting at least one parent with a history of gambling problems. These findings suggest that problems associated with gambling are a university-wide issue with student-athletes meriting additional attention because of implications for the integrity of intercollegiate sports. Improved prevention and intervention efforts for collegiate gambling are recommended.