Uniform Test for Predictive Regression With AR Errors

Testing predictability is of importance in economics and finance. Based on a predictive regression model with independent and identically distributed errors, some uniform tests have been proposed in the literature without distinguishing whether the predicting variable is stationary or nearly integrated. In this article, we extend the empirical likelihood methods of Zhu, Cai, and Peng with independent errors to the case of an AR error process. Again, the proposed new tests do not need to know whether the predicting variable is stationary or nearly integrated, and whether it has a finite variance or an infinite variance. A simulation study shows the new methodologies perform well in finite sample.     

A propensity score matching analysis of the relationship between victim sex and capital juror decision-making in North Carolina

A small body of prior research has examined the impact of victim sex on jury death penalty decision-making and the majority of this research has demonstrated some evidence of a “female victim effect” such that cases involving a female victim are more likely to receive the death penalty than similarly situated cases with a male victim. However, within this line of research studies have suggested that victim sex may work in conjunction with other case characteristics. In order to further explore this phenomenon, the current study examines a near-population of death penalty cases from North Carolina (n = 1069) from 1977–2009 using propensity score matching. Results demonstrate that once cases are matched on more than 50 legal and extralegal case characteristics, there is no statistically significant or substantive link between victim sex and death penalty decision-making. Findings suggest that it is concrete differences in the legal and extralegal factors observed in cases with female victims compared to male victims that shape jury death sentence decisions rather than a direct effect of victim sex (before matching: OR = 1.53; 95% CI = 1.20–1.95; p < .001/after matching: OR = 0.90; 95% CI = 0.66–1.24; p = .52). Study limitations and implications are also discussed.     

Poverty and educational achievement in the US: A less-biased estimate using PISA 2012 data

In the current paper, we employ the most recent Programme for International Student Assessment (PISA) data to calculate a less-biased estimate of poverty on US achievement. The PISA was specifically chosen as it is an assessment removed from a specific curriculum and instead focuses on concepts that students should know in order to participate in a global economy. Using a propensity score matching approach, our findings suggest that US students in poverty have notable educational attainment deficiencies compared to a matched group of students who are not in poverty. In other words, when we select two students who have a great deal in common but for the fact that one comes from a poverty background, the student in poverty is expected to perform nearly 28 points, or about a quarter of a standard deviation lower, on the PISA assessment. In real terms, this puts math achievement for children not in poverty on-par with the Organisation for Economic Co-operation and Development (OECD) average, while children in poverty are well below the OECD average.     

A comparative study of confidence intervals for negative binomial proportion

In this paper, we investigate four existing and three new confidence interval estimators for the negative binomial proportion (i.e., proportion under inverse/negative binomial sampling). An extensive and systematic comparative study among these confidence interval estimators through Monte Carlo simulations is presented. The performance of these confidence intervals are evaluated in terms of their coverage probabilities and expected interval widths. Our simulation studies suggest that the confidence interval estimator based on saddlepoint approximation is more appealing for large coverage levels (e.g., nominal level≤1% ) whereas the score confidence interval estimator is more desirable for those commonly used coverage levels (e.g., nominal level>1% ). We illustrate these confidence interval construction methods with a real data set from a maternal congenital heart disease study.     

Mean shift and influence measures in linear measurement error models with stochastic linear restrictions

We present influence diagnostics for linear measurement error models with stochastic linear restrictions using the corrected likelihood of Nakamura in 1990. The case deletion and mean shift outlier models are developed to identify outlying and influential observations. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. The analogs of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion models. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to evaluate the performance of the proposed estimators based on the mean squares error criterion and the score test statistic. Finally, a numerical example is given to illustrate the theoretical results.     

The effect of sociodemographic (mis)match between interviewers and respondents on unit and item nonresponse in Belgium

Interviewer characteristics affect nonresponse and measurement errors in face-to-face surveys. Some studies have shown that mismatched sociodemographic characteristics – for example gender – affect people's behavior when interacting with an interviewer at the door and during the survey interview, resulting in more nonresponse. We investigate the effect of sociodemographic (mis)matching on nonresponse in two successive rounds of the European Social Survey in Belgium. As such, we replicate the analyses of the effect of (mis)matching gender and age on unit nonresponse on the one hand, and of gender, age and education level (mis)matching on item nonresponse on the other hand. Recurring effects of sociodemographic (mis)match are found for both unit and item nonresponse.     

A Statewide Parenting Alternative Sentencing Program: Description and Preliminary Outcomes

ABSTRACT

The Washington State Legislature created the Parenting Sentencing Alternative in 2010, authorizing a substitute to total confinement for parents of minor children. The Alternative is designed to strengthen family bonds and improve parenting skills to encourage successful reintegration. An overview of the Alternative’s history, design, and implementation is presented, followed by preliminary results from an impact evaluation. A case study of a successful participant is presented, and implications for the findings are discussed.     

Interval estimation for the breakpoint in segmented regression: a smoothed score‐based approach

This paper is concerned with interval estimation for the breakpoint parameter in segmented regression. We present score‐type confidence intervals derived from the score statistic itself and from the recently proposed gradient statistic. Due to lack of regularity conditions of the score, non‐smoothness and non‐monotonicity, naive application of the score‐based statistics is unfeasible and we propose to exploit the smoothed score obtained via induced smoothing. We compare our proposals with the traditional methods based on the Wald and the likelihood ratio statistics via simulations and an analysis of a real dataset: results show that the smoothed score‐like statistics perform in practice somewhat better than competitors, even when the model is not correctly specified.     

Asymptotic relative efficiency of wald tests in measurement error models

In this paper, asymptotic relative efficiency (ARE) of Wald tests for the Tweedie class of models with log-linear mean, is considered when the aux¬iliary variable is measured with error. Wald test statistics based on the naive maximum likelihood estimator and on a consistent estimator which is obtained by using Nakarnura's (1990) corrected score function approach are defined. As shown analytically, the Wald statistics based on the naive and corrected score function estimators are asymptotically equivalents in terms of ARE. On the other hand, the asymptotic relative efficiency of the naive and corrected Wald statistic with respect to the Wald statistic based on the true covariate equals to the square of the correlation between the unobserved and the observed co-variate. A small scale numerical Monte Carlo study and an example illustrate the small sample size situation.     

Efficiency or Competition? A Structural Econometric Analysis of Canada's AWS Auction and the Set‐Aside Provision

In 2008, Industry Canada auctioned 105 MHz of spectrum to a group of bidders that included incumbents and potential new entrants into the Canadian mobile phone market, raising $4.25 billion. In an effort to promote new entry, 40 MHz of spectrum was set‐aside for new entrants. In order to estimate the implicit cost of the set‐aside provision, we estimate the parameters of the bidders' profit function via a maximum match estimator based on the notion of pairwise stability in matches. We find that all telecommunications firms valued both geographic complementarities across auction licenses as well as absolute spectrum. Under a reasonable alternative scenario, our results indicate that the set‐aside led to a total profit loss of approximately 10%.