A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Ownership structure and hospital service costs and fees: A decomposition approach

This paper analyzes hospital characteristics that are associated with higher average costs and charges for venipuncture, computed tomography procedures (computerized axial tomography [CAT] scans), and electrocardiograms (EKG). Using data from a Medicare database, our results indicate that higher wages, larger hospital sizes, and greater service quality are associated with higher procedure costs, whereas system membership is generally associated with lower procedure costs. Blinder‐type decompositions, which are the main focus of this study, suggest (a) that venipuncture costs are about 17% to 19% lower at proprietary hospitals than at nonprofit or government hospitals, (b) CAT scan costs are about 6% to 12% lower at nonprofit hospitals than at proprietary and government hospitals, and (c) that EKG costs are about 3% lower at proprietary hospitals than at nonprofit or government hospitals. Lastly, large portions of each of these differences are found to be due to both differences in mean values of the hospitals' characteristics by ownership type and differences in the mechanism by which the hospitals' characteristics are transmitted to procedure costs.     

“Going natural”: Black women’s identity project shifts in hair care practices

Hair care, a seemingly mundane consumption practice, is packed with sociocultural meanings and constitutes an important realm of identity work for consumers. Drawing upon existing literature on consumer identity projects, this paper investigates the experiences of urban, Black women in Kenya as they disengage from the normative practice of altering their natural hair texture using chemical straighteners (a practice that conforms to Eurocentric beauty ideals of straight, flowing hair), and adopt the non-normative hair care practice of “going natural,” in which they embrace their natural hair texture and hairstyles. This paper traces the historical, sociocultural, and political events that underpin the normative ideology associated with the identity marker of natural Black hair, and how this ideology influences the women’s “going natural” experience. Findings reveal tensions that emerge in the women’s identity project disengagement and reconstruction process, and how the women navigate their position as they reimagine their embodied identity quest.     

Tobacco retail licences and state formation in early modern England and Wales

This article addresses one under‐studied aspect of Charles I's finances during his Personal Rule: the licensing of tobacco retailers. While it was ultimately a failed project, the tobacco retail licence project was fiscally successful before the transformative events of the 1640s triggered its demise. The project enabled tobacco retail licensees to establish commercial outlets for the marketing of tobacco throughout England and Wales, and cooperation with pre‐existing officeholders contributed to the apprehension of unlicensed retailers. Ultimately, the geographic breadth of tobacco licences translated into much‐needed royal revenue which, when added to other projects and patents, contributed to the king's financial survival. The evidence presented here suggests that we may want to rethink some of our assumptions for how the process of state formation worked and that earlier seventeenth‐century ‘prototypes’ of taxation were more fiscally successful than previously recognized.     

Sharing versus collaborative economy: how to align ICT developments and the SDGs in tourism?

Great hopes have been placed in the sharing economy to provide a new business model based on peer-to-peer (P2P) exchanges of underutilized assets. As a model, the sharing economy has been expected to make significant contributions to sustainability, providing new opportunities for entrepreneurship, more sustainable use of resources, and consumer co-operation in tight economic networks. However, in recent years, digital platforms have turned into the most important actors in the global sharing economy, turning global corporations, such as AirBnB, Booking, or TripAdvisor into intermediaries controlling and profiting from most transactions. Focused on accommodation, this paper conceptualizes the sharing economy in comparison to the wider collaborative economy, and discusses its social, economic, environmental, and political impacts in comparison to the sustainable development goals. It concludes that the sharing economy has great potential to make very significant contributions to sustainability, though the model is increasingly being replaced by the collaborative economy, which performs as an extension and acceleration of neoliberal economic practices.     

Passive Investment Strategies and Efficient Markets

This paper presents the case for and the evidence in favour of passive investment strategies and examines the major criticisms of the technique. I conclude that the evidence strongly supports passive investment management in all markets—small–capitalisation stocks as well as large–capitalisation equities, US markets as well as international markets, and bonds as well as stocks. Recent attacks on the efficient market hypothesis do not weaken the case for indexing.     

Earnings management and abnormal returns: Evidence from the 1970–1972 Price Control Regulations

We examine the association between abnormal returns and earnings management in the context of price control regulations to test the construct validity of the earnings management model. Abnormal returns are used as a market–based measure, and discretionary accruals are employed to measure earnings management. Our results support the hypotheses that (1) price control regulations affect firms' security prices negatively, (2) firms make income–decreasing discretionary accruals to increase the likelihood of price increase approval, and (3) firms that are affected most negatively by the regulations manage earnings more aggressively. We conclude that the earnings management model we use in this study is capable of predicting opportunistic discretionary accruals.     

Testing Rival Theories of Budgetary Decision–making in the US States

This article tests three common budgetary decision–making theories in the US states. Pooled time series cross–section analysis is used from 1960 to 1996 to test the garbage can theory and incrementalism, and from 1989 to 1996 to test various theories of rational budgeting. The results demonstrate that there was some support for all three theories in terms of their impact on reducing state budget outputs. Rational budgeting reduces expenditures in aggregate, incrementalism has relatively low explanatory power below the aggregates, and garbage can budgeting is more prevalent in functional areas than for government as a whole. These findings imply that the future research agenda on budgetary decision–making theories should focus more on a system–wide approach, which takes into account many of the characteristics of all three rival models of decision–making, rather than exclusively focusing on each one singly.     

Density estimation in the uniform deconvolution model

We consider the problem of estimating a probability density function based on data that are corrupted by noise from a uniform distribution. The (nonparametric) maximum likelihood estimator for the corresponding distribution function is well defined. For the density function this is not the case. We study two nonparametric estimators for this density. The first is a type of kernel density estimate based on the empirical distribution function of the observable data. The second is a kernel density estimate based on the MLE of the distribution function of the unobservable (uncorrupted) data.