Peter Schmidt: Econometrician and consummate professional

Peter Schmidt has been one of its best-known and most respected econometricians in the profession for four decades. He has brought his talents to many scholarly outlets and societies, and has played a foundational and constructive role in the development of the field of econometrics. Peter Schmidt has also served and led the development of Econometric Reviews since its inception in 1982. His judgment has always been fair, informed, clear, decisive, and constructive. Respect for ideas and scholarship of others, young and old, is second nature to him. This is the best of traits, and Peter serves as an uncommon example to us all. The seventeen articles that make up this Econometric Reviews Special Issue in Honor of Peter Schmidt represent the work of fifty of the very best econometricians in our profession. They honor Professor Schmidt's lifelong accomplishments by providing fundamental research work that reflects many of the broad research themes that have distinguished his long and productive career. These include time series econometrics, panel data econometrics, and stochastic frontier production analysis.     

Predicting distances using a linear model: the case of varietal distinctness

Differences between plant varieties are based on phenotypic observations, which are both space and time consuming. Moreover, the phenotypic data result from the combined effects of genotype and environment. On the contrary, molecular data are easier to obtain and give a direct access to the genotype. In order to save experimental trials and to concentrate efforts on the relevant comparisons between varieties, the relationship between phenotypic and genetic distances is studied. It appears that the classical genetic distances based on molecular data are not appropriate for predicting phenotypic distances. In the linear model framework, we define a new pseudo genetic distance, which is a prediction of the phenotypic one. The distribution of this distance given the pseudo genetic distance is established. Statistical properties of the predicted distance are derived when the parameters of the model are either given or estimated. We finally apply these results to distinguishing between 144 maize lines. This case study is very satisfactory because the use of anonymous molecular markers (RFLP) leads to saving 29% of the trials with an acceptable error risk. These results need to be confirmed on other varieties and species and would certainly be improved by using genes coding for phenotypic traits.     

Estimation of long-run inefficiency levels: a dynamic frontier approach

Cornwell, Schmidt, and Sickles (1990) and Kumbhakar (1990), among others, developed stochasticfrontier production models which allow firm specific inefficiency levels to change over time. These studies assumed arbitrary restrictions on the short-run dynamics of efficiency levels which have little theoretical justification. Further, the models are inappropriate for estimation of long-run efficiencies. We consider estimation of an alternative frontier model in which firmspecific technical inefficiency levels are autoregressive. This model is particularly useful to examine a potential dynamic link between technical innovations and production inefficiency levels. We apply our methodology to a panel of US airlines.     

Shrinkage confidence intervals for the normal mean: Using a guess for greater efficiency

If the unknown mean of a univariate population is sufficiently close to the value of an initial guess then an appropriate shrinkage estimator has smaller average squared error than the sample mean. This principle has been known for some time, but it does not appear to have found extension to problems of interval estimation. The author presents valid two‐sided 95% and 99% “shrinkage” confidence intervals for the mean of a normal distribution. These intervals are narrower than the usual interval based on the Student distribution when the population mean lies in such an “effective interval.” A reduction of 20% in the mean width of the interval is possible when the population mean is sufficiently close to the value of the guess. The author also describes a modification to existing shrinkage point estimators of the general univariate mean that enables the effective interval to be enlarged.     

A mixture model for random graphs

The Erdös–Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.     

Differences in Adolescent Experiences of Polyvictimization and Suicide Risk by Sexual Minority Status

Sexual minority adolescents (SMA) report more suicide risk behaviors than heterosexual adolescents. Polyvictimization (co‐occurrence of multiple types of victimization) may be an important, underresearched correlate of this disparity. With the 2017 national Youth Risk Behavior Survey (N = 13,179), national estimates of polyvictimization and suicide risk were assessed among high school students by sexual minority status (SM vs. heterosexual), and multivariate relationships between sexual minority status, polyvictimization, and suicide risk were tested. Additionally, risk profiles of those who experienced polyvictimization (2 + types of victimization; n = 1,932) were compared across sexual minority status. Results confirm that SMA are more likely to experience polyvictimization than heterosexual adolescents (31.8% v. 12.9%, respectively); however, also indicate that polyvictimization does not fully explain elevated suicide risk among SMA.     

Children's Differentiation Between Ingratiation and Self-Promotion

Previous research has demonstrated that 10-year-olds can provide interpersonal explanations for certain self-presentational tactics, but detailed information about the development of their understanding of these tactics is lacking. This research investigated children's understanding of the processes involved in ingratiation (used to indicate likeability) and self-promotion (used to indicate competence). In the first study, with a sample of 60 children aged six to 11 years, children saw ingratiation as leading to more positive social evaluation than self-promotion, which was seen as having a more concrete, instrumental function. Additionally, children's differentiation between ingratiation and self-promotion was correlated with their level of peer preference, as determined through sociometric nominations, particularly for boys. In a second study, with a sample of 63 children aged six to 11 years, it was found that audience type (peer vs. adult) was related to children's understanding of the self-presentational tactics: children offered more social evaluation justifications for a self-promotion tactic when the audience was a peer rather than an adult. Results are discussed with reference to emerging insights into the links between peer relations and social cognition.