Exploring the Behavior of Economic Agents: the role of relative preferences

Standard economic theory assumes individuals choose actions that optimize their expected utility. In this paper we investigate how the existence of players with non-standard preferences may influence economic agents' behavior in some of the most frequently studied non-cooperative games. We find that allowing for the existence of agents with relative preferences can help explain observed economic actions which, at times, appear counter-intuitive.

Fast, effective, and coherent time series modeling using the sparsity-ranked lasso

The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorithm should be more skeptical of higher-order polynomials and interactions *a priori* compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In time series, the same idea of ranked prior skepticism can be applied to the possibly seasonal autoregressive (AR) structure of the series during the model fitting process, becoming especially useful in settings with uncertain or multiple modes of seasonality. The SRL can naturally incorporate exogenous variables, with streamlined options for inference and/or feature selection. The fitting process is quick even for large series with a high-dimensional feature set. In this work, we discuss both the formulation of this procedure and the software we have developed for its implementation via the **srlTS** R package. We explore the performance of our SRL-based approach in a novel application involving the autoregressive modeling of hourly emergency room arrivals at the University of Iowa Hospitals and Clinics. We find that the SRL is considerably faster than its competitors, while producing more accurate predictions

A Large Sample Comparison of Grade Based Student Learning Outcomes in Online vs. Face-to-Face Courses

Comparisons of grade based learning outcomes between online and face-to-face course formats have become essential because the number of online courses, online programs and institutional student enrollments have seen rapid growth in recent years. Overall, online education is largely viewed by education professionals as being equivalent to instruction conducted face-to-face. However, the research investigating student performance in online versus face-to-face courses has been mixed and is often hampered by small samples or a lack of demographic and academic controls. This study utilizes a dataset that includes over 5,000 courses taught by over 100 faculty members over a period of ten academic terms at a large, public, four-year university. The unique scale of the dataset facilitates macro level understanding of course formats at an institutional level. Multiple regression was used to account for student demographic and academic corollaries--factors known to bias course format selection and grade based outcomes--to generate a robust test for differences in grade based learning outcomes that could be attributed to course format. The final model identified a statistical difference between course formats that translated into a negligible difference of less than 0.07 GPA points on a 4 point scale. The primary influence on individual course grades was student GPA. Interestingly, a model based interaction between course type and student GPA indicated a cumulative effect whereby students with higher GPAs will perform even better in online courses (or alternatively, struggling students perform worse when taking courses in an online format compared to a face-to-face format). These results indicate that, given the large scale university level, multi course, and student framework of the current study, there is little to no difference in grade based student performance between instructional modes for courses where both modes are applicable

State-Space Models for Binomial Time Series with Excess Zeros

Count time series with excess zeros are frequently encountered in practice. In characterizing a time series of counts with excess zeros, two types of models are commonplace: models that assume a Poisson mixture distribution, and models that assume a binomial mixture distribution. Extensive work has been published dealing with modeling frameworks based on Poisson-type approaches, yet little has concentrated on binomial-type methods. To handle such data, we propose two general classes of time series models: a class of observation-driven ZIB (ODZIB) models, and a class of parameter-driven ZIB (PDZIB) models. The ODZIB model is formulated in the partial likelihood framework, which facilitates model fitting using standard statistical software for ZIB regression models. The PDZIB model is conveniently formulated in the state-space framework. For parameter estimation, we devise a Monte Carlo Expectation Maximization (MCEM) algorithm, with particle filtering and particle smoothing methods employed to approximate the intractable conditional expectations in the E-step of the algorithm. We investigate the efficacy of the proposed methodology in a simulation study, which compares the performance of the proposed ZIB models to their counterpart zero-inflated Poisson (ZIP) models in characterizing zero-inflated count time series. We also present a practical application pertaining to disease coding

A New Bootstrap Goodness-of-Fit Test for Normal Linear Regression Models

In this work, the distributional properties of the goodness-of-fit term in likelihood-based information criteria are explored. These properties are then leveraged to construct a novel goodness-of-fit test for normal linear regression models that relies on a non-parametric bootstrap. Several simulation studies are performed to investigate the properties and efficacy of the developed procedure, with these studies demonstrating that the bootstrap test offers distinct advantages as compared to other methods of assessing the goodness-of-fit of a normal linear regression model

Variation in Student Perceptions of Higher Education Course Quality and Difficulty as a Result of Widespread Implementation of Online Education During the COVID-19 Pandemic

The onset of the COVID-19 global pandemic affected higher education in a myriad of ways. One of the most notable effects however was the rapid and sudden transition of nearly all courses at most institutions to an online environment. And while there are a growing number of courses offered online already, this transition to nearly 100% remote education presented numerous challenges for instructors and students of face-to-face and hybrid style courses. Since student perceptions are closely tied to recruitment and retention, it is important to know if there are differences in student perceptions present in the way different courses are taught. This study extends the work of other authors that have investigated student perceptions by looking specifically at how the COVID-19 pandemic may have changed student views of course difficulty and quality both overall and across discipline or institution categories. Course evaluations from 837 courses from 191 different schools archived on RateMyProfessors.com were used in a general linear model where a statistically significant overall decline of 6% in perceived course difficulty and 4% decline in perceived quality was detected. In addition to calculating this mean decrease, courses were also categorized on the basis of academic discipline (Business, Engineering and Mathematics, Humanities, Natural Sciences, Social Sciences), institution type (2-Year, 4-Year), and whether instructors had previous experience teaching online courses (No, Yes) to determine any variation in differences that may have appeared as a result of more nuanced details in course type or delivery. Most notably, declines in course difficulty were slightly more apparent with instructors that had no previous online teaching experience. No other discipline, institution type, or teaching experience interactions were detected with either difficulty or quality variation. These data suggest that there were very real changes in perceived quality and difficulty but that these changes were largely universal irrespective of discipline, institution type, or prior experience teaching online (with exception of course difficulty)

ARE ONLINE COURSES CANNIBALIZING STUDENTS FROM EXISTING COURSES?

One of the reasons most often cited for the increasing number and popularity of online courses is the format’s ability to provide access to students who cannot attend conventionally delivered face-to-face courses. Are these underserved students in fact the ones enrolling in online courses? Or are online course enrollees the same students who would otherwise be taking face-to-face courses? This analysis uses student registration information from six different online courses at two campuses of a Midwestern university to investigate how students taking online courses compare to the entire student population. In particular, this study addresses whether or not students take online courses to eliminate significant commuting time when they are located long distances from campus

Stretch Increases Alveolar Epithelial Permeability to Uncharged Micromolecules

We measured stretch-induced changes in transepithelial permeability in vitro to uncharged tracers 1.5–5.5 Å in radius to identify a critical stretch threshold associated with failure of the alveolar epithelial transport barrier. Cultured alveolar epithelial cells were subjected to a uniform cyclic (0.25 Hz) biaxial 12, 25, or 37% change in surface area (ΔSA) for 1 h. Additional cells served as unstretched controls. Only 37% ΔSA (100% total lung capacity) produced a significant increase in transepithelial tracer permeability, with the largest increases for bigger tracers. Using the permeability data, we modeled the epithelial permeability in each group as a population of small pores punctuated by occasional large pores. After 37% ΔSA, increases in paracellular transport were correlated with increases in the radii of both pore populations. Inhibition of protein kinase C and tyrosine kinase activity during stretch did not affect the permeability of stretched cells. In contrast, chelating intracellular calcium and/or stabilizing F-actin during 37% ΔSA stretch reduced but did not eliminate the stretch-induced increase in paracellular permeability. These results provide the first in vitro evidence that large magnitudes of stretch increase paracellular transport of micromolecules across the alveolar epithelium, partially mediated by intracellular signaling pathways. Our monolayer data are supported by whole lung permeability results, which also show an increase in alveolar permeability at high inflation volumes (20 ml/kg) at the same rate for both healthy and septic lungs