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The COVID-19 pandemic required adaptation to a new learning environment creating challenges for students and instructors. A reduction in student-teacher contact and the lack of supervision should have led to a decline in students’ academic performance. Nonetheless, studies report increases in grades during the pandemic. Yet, limited information is available regarding the persistence of this impact. This study utilizes a hierarchical mixed effect model to estimate the impact of the COVID-19 pandemic on university grades. Using unique class-level data containing chronological variables and institutional, instructor, and student characteristics, spanning Fall 2010 to Spring 2021 of 7,852 undergraduate classes, it is shown class average grade point averages (GPAs) in the College of Agriculture at Texas A&M University increased for the three semesters most impacted by COVID-19. Average class GPAs increased by 0.22 points in Spring 2020 because of COVID-19 and then approximately 0.18 points in the subsequent next two semesters. The negative effect of class size decreased during COVID-19, implying online classes have different size effects than traditional classes. Additionally, the positive effect of SAT scores on grades decreased. One implication of this study is that COVID-19 may not only have a direct, significant, impact on GPAs but may also indirectly affect GPAs through altering the effects of variables on GPAs. The causal mechanisms by which the changes occurred are an area for further research. JEL Codes: I21, I29

An overview of the literature considering committed quantities in demand estimation for various commodities with an emphasis on energy commodities is presented. This overview provides a definition and the history of committed quantities, along with different theoretical modeling methodologies. Committed quantities are quantities that are consumed in the short run with little regard for price. Previous studies suggest that committed quantities for various commodities range from 15 to 98% of consumption. The inclusion of committed quantities appears to improve estimates generally, but it is not clear-cut. Problems arise when estimated committed quantities are negative or larger than the consumption amount. This review concludes with a recommendation that further research is necessary to resolve such issues, provide an improved understanding of the committed quantities in estimation, and fill in knowledge gaps concerning committed quantities ranging from theoretical to practical issues.

In the present paper, an investigation into Thailand’s energy demand is performed to determine if: (1) a linear or nonlinear Engel curve better explains the relationship between income and energy consumption, and (2) systems with pre-commitments better model energy consumptions. Four demand systems are estimated: an almost ideal demand system (AIDS), the quadratic almost ideal demand system (QAIDS), generalized almost ideal demand system (GAIDS), and the generalized quadratic almost ideal demand system (GQAIDS). Elasticities are calculated for policy implications. The empirical results suggest that models considering pre-commitments and nonlinear Engel curves may be slightly more appropriate for Thailand’s energy system, from both statistic and economic standpoints. Statistical inferences appear to favor the GQAIDS model based on the encompassing results. Economic reasonability also appears to favor the GQAIDS model, in particular, petroleum products, as it provides results consistent with the notions of precommitments and fuel substitutability found in previous studies. Most of the previous studies in various forms have shown that the demand for petroleum products is relatively inelastic to price in Thailand. The current study, however, finds that own-price elasticities of uncompensated demand for petroleum products are almost unitary, which is relatively more elastic than most of the previous studies. As such, further studies are required and the price-based policy on petroleum products targeting the reduction in petroleum product dependence must be implemented with caution.

Mobility remains a vital part of the well-being of rural-living, older adults and transportation disadvantaged persons. This study seeks to identify research and policy needs related to rural transit for older people and the transportation disadvantaged. To obtain this goal, the multidisciplinary study team conducted two activities as part of a 2016 rural transportation conference: a survey of conference attendees and open discussion to elicit additional information. Results suggest the attendees felt the need for rural transit for older adults would continue to increase with public and private funding being critical issues. Respondents had similar opinions about challenges and opportunities across socioeconomic characteristics including age, gender, political leaning, rurality, and organizational function. This suggests an opportunity to mobilize support for public transportation.

Causal flow analyses combined with time series analyses are used to examine price relationships among fresh broiler retail markets (Northeast, South, Midwest, and West). Results indicate structural changes have occurred in this industry. Reasons for changes in price relationships include the perishable nature of fresh broilers, along with vertical integration and increases in production and concentration in the industry. The four markets are integrated, but the level of integration has decreased over time. With the markets becoming more exogenous, there may be a decrease in society’s welfare. The South market is the most important market for price discovery.

The natural gas sector has undergone major regulatory and technological changes. These changes may induce structural changes in price relationships among natural gas markets. Tests for structural breaks suggest two potential structural breaks, around 2000 and 2009. Previous forecasting studies on natural gas prices/returns largely are point forecasts and focus on a single spot market; unlike those, this study undertakes simultaneous probabilistic forecasts of eight spot markets. Prequential forecasting analysis examines: (1) whether differences exist in the ability to probabilistically forecast returns among various natural gas markets and (2) how the presence of structural breaks in the natural gas sector influences the probability forecasts. The ability to forecast natural gas markets differs based on the different criteria. Disparities may be explained by each market’s role in price discovery, the alteration of the market’s participation, and whether the market is located in an excess supply or demand region. Irrespective of the models, Henry Hub and AECO returns appear to be easier to forecast, as they generally have the smaller root-mean-squared error, Brier score, and ranked probability score, while Dominion South and Chicago returns appear to be more difficult to forecast. Models using longer periods of data appear to forecast returns better than models using data starting after the breaks; the latter always produces the largest root-mean-squared error, Brier score, and ranked probability score.

Purpose – What copulas are, their estimation, and use is illustrated using a geographical diversification example. To accomplish this, dependencies between county-level yields are calculated for non-irrigated wheat, upland cotton, and sorghum using Pearson linear correlation and Kendall's tau. The use of Kendall's tau allows the implementation of copulas to estimate the dependency between county-level yields. The paper aims to discuss these issues. Design/methodology/approach – Four parametric copulas, Gaussian, Frank, Clayton, and Gumbel, are used to estimate Kendall's tau. These four estimates of Kendall's tau are compared to Pearson's linear correlation, a more typical measure of dependence. Using this information, functions are estimated to determine the relationships between dependencies and changes in geographic and climate data. Findings – The effect on county-level crop yields based on changes of geographical and climate variables differed among the different dependency measures among the three different crops. Implementing alternative dependency measures changed the statistical significance and the signs of the coefficients in the sorghum and cotton dependence functions. Copula-based elasticities are consistently less than the linear correlation elasticities for wheat and cotton. For sorghum, however, the copula-based elasticities are generally larger. The results indicate that one should not take the issue of measuring dependence as a trivial matter. Originality/value – This research not only extends the current literature on geographical diversification by taking a more detailed examination of factors impacting yield dependence, but also extends the copula literature by comparing estimation results using linear correlation and copula-based rank correlation.

Conditional forecasts of US economic and energy sector activity are developed using information from a dynamic, data-rich environment. The forecasts are conditional on a path for carbon dioxide emissions outlined in the US Environmental Protection Agency’s Clean Power Plan (CPP) and are estimated based on a factor-augmented autoregressive framework. Results suggest that overall growth will be slower under the CPP than it would otherwise; however, economic growth and CO2 reductions can be achieved simultaneously. There are little differences between unconditional (business-as-usual) and conditional forecasts of the variables in the early part of the forecast period; the impacts of the CPP are small while the constraints on carbon dioxide are less stringent. The results serve as a data-driven complement to structural analyses of policy change in the energy sector.

A dynamic system consisting of donations and various sports' success is estimated to examine relationships among winning and donations. Differences between Power 5, Non-power 5, and Football Championship Series alignments exist. Evidence that successful athletic programs have a positive impact on donations is found regardless of the conference alignment, but the impact varies by sport and alignment. The effects, however, are short lived. Donations, however, appear to have little impact on winning. There is some degree of interaction among winning in different sports.

Extracting information from high-dimensional time series in the form of underlying factors is an increasingly popular methodology in forecasting applications. In this paper, principal component analysis (PCA) and three other methods for factor extraction are compared based on their deterministic and probabilistic forecasting performances using factor-augmented vector autoregressive (FAVAR) models. The existing PCA-based methods use only the contemporaneous covariance matrix of the data, while the other methods rely on weighted lagged cross-covariance matrices. Our empirical study considers four crude oil future price instruments and a 241 variable dataset of global energy prices and quantity, macroeconomic indicators, and financial series which are thought to influence oil price movements. Overall empirical findings are: (1) the PCA-based method performs better at shorter forecast horizons whereas the new methods involving lagged cross-covariance matrices tend to perform better at longer horizons (2 months or greater); (2) the performance ranking of the four methods under both deterministic and probabilistic forecasting is greatly affected by the number of factors included in the FAVAR models; (3) the forecast performances of the four methods are close to each other and no method performs uniformly better than the others. More research on the role of temporal dependence in determining the number of factors is warranted.