Nonlinearity in Global Crude Oil Benchmarks: Disentangling the Effect of Time Aggregation

We model the first and second moments of global crude oil benchmarks, using iterative pre-whitened generalized autoregressive conditional heteroskedasticity (GARCH) models and, in doing so, validate the efficacy of such models in assimilating the neglected nonlinearities in the underlying data-generating processes. The benchmarks considered for this study are Brent, Dubai/Oman, and West Texas Intermediate (WTI) crude oil. While nonlinear serial dependence happens to be a stylized fact across different asset classes, it is our view that prior scholarly contributions have not adequately untangled the effect of data aggregation (in time) in the examination of nonlinear dependencies. In this context, the present study strives to untangle the critical role that time aggregation plays in the examination of nonlinearity in global crude oil benchmarks using data at daily, weekly as well as monthly time frequencies. Our findings are as follows: the optimum GARCH models perform well in capturing all of the neglected nonlinearity in monthly returns of the crude benchmarks. When it comes to daily and weekly returns, our study reveals traces of neglected nonlinearities that are not completely captured by GARCH models. Moreover, such residual traces of neglected nonlinear dependencies are relatively more pronounced at the granular levels and become more and more elusory as the data get aggregated in time. JEL codes: C22, C53, C58, G1, Q47

Spatiotemporal Evolution Patterns of the COVID-19 Pandemic Using Space-Time Aggregation and Spatial Statistics: A Global Perspective

Unlike previous regionalized studies on a worldwide crisis, this study aims to analyze spatial distribution patterns and evolution characteristics of the COVID-19 pandemic, using space-time aggregation and spatial statistics from a global perspective. Hence, various spatial statistical methods, such as the heat map, global Moran’s I, geographic mean center, and emerging hot spot analysis were utilized comprehensively to mine and analyze spatiotemporal evolution patterns. The main findings were as follows: Overall, the spatial autocorrelation of confirmed cases gradually increased from the initial outbreak until September 2020 and then decreased slightly. The geographic centroid migration ranges of the pandemic in Asia, Europe, and Africa are wider than those in South America, Oceania, and North America. The spatiotemporal evolution pattern of the global pandemic mainly consisted of oscillating hot spots, intensifying cold spots, persistent cold spots, and diminishing cold spots. This study provides auxiliary decision-making information for pandemic prevention and control.

EXPRESS: Choosing the Light Meal: Real-time Aggregation of Calorie Information Reduces Meal Calories

Numeric labeling of calories on restaurant menus has been implemented widely, but scientific studies have generally not found substantial effects on calories ordered. The present research tests the impact of a feedback format that is more targeted at how consumers select and revise their meals: real-time aggregation of calorie content to provide dynamic feedback about meal calories via a traffic light label. Because these labels intuitively signal when a meal shifts from healthy to unhealthy (via the change from green to a yellow or red light), they prompt decision makers to course correct in real time, before they finalize their choice. Results from five pre-registered experiments ( N = 11,900) show that providing real-time traffic light feedback about the total caloric content of a meal reduces calories in orders, even compared to similar aggregated feedback in numeric format. Patterns of ordering reveal this effect to be driven by people revising high-calorie orders more frequently, leading them to choose fewer and lower-calorie items. Consumers also like traffic light aggregation, indicating greater satisfaction with their order and greater intentions to return to restaurants that use them. The authors discuss how dynamic feedback using intuitive signals could yield benefits in contexts beyond food choice.