Are the macroeconomic effects on oil price asymmetric? An asymmetric quantile regression approach

Purpose This paper aims to contribute to the clarification of whether the dependence and causality between oil and the macrofundamentals change across different quantiles of the distribution function. Design/methodology/approach Within the context of an asymmetric quantile approach, we drop the assumption that variables operate at the upper tails of the distribution in the way that they operate at the mean. Findings Our innovative approach indicates that the response of oil prices not only differs according to the underlying source of the variables shock but also differs across the quantiles. Originality/value Although a number of recent studies are closely related to our present research, our novel findings offer some important insights that foreshadow the empirical results. The current research addresses to answer the following questions, in sequence: (i) Is there any extreme value dependence between the crude oil and macroeconomic variables? If yes, (ii) is the dependence symmetric or asymmetric? Finally, (iii) can this dependence be driven by the phases of the economic cycle?

Spatio-Temporal Impact of Global Migration on Carbon Transfers Based on Complex Network and Stepwise Regression Analysis

Considering the advancement of economic globalization, the reasons for migration together with the lifestyles of migrants will change the use of energy, environment of origin and destination. This study therefore explores the patterns of global trade-induced carbon emission transfers using “center-of-gravity” and complex network analysis. We further investigate the determinants of carbon transfers by integrating the impact of population migration through the STIRPAT framework for 64 countries over the period 2005–2015 using the stepwise regression approach. Our results unveil that higher levels of migration flow induce higher carbon flow. Specifically, every 1% increase in migration, triggers carbon transfers to increase within the range of 0.118%−0.124%. The rising impact of migration cannot be ignored, even though the coefficients were not so high. Besides, for both male and female migrants, their impact on carbon transfers generated by the intermediate products were higher than those generated by the final products. However, the influence is more obvious in male migrants. With the aim of dividing the sample of countries into three income groups, the results generally show that the impacts of migration vary across levels of income. Therefore, the environmental pressure caused by immigration should be considered by destination countries in the formulating of migration policies. On the other hand, origin countries should take some responsibility for carbon emissions according to their development characteristics.

Optimizing Photoperiod Switch to Maximize Floral Biomass and Cannabinoid Yield in Cannabis sativa L.: A Meta-Analytic Quantile Regression Approach

Cannabis sativa L. is an annual, short-day plant, such that long-day lighting promotes vegetative growth while short-day lighting induces flowering. To date, there has been no substantial investigation on how the switch between these photoperiods influences yield of C. sativa despite the tight correlation that plant size and floral biomass have with the timing of photoperiod switches in indoor growing facilities worldwide. Moreover, there are only casual predictions around how the timing of the photoperiodic switch may affect the production of secondary metabolites, like cannabinoids. Here we use a meta-analytic approach to determine when growers should switch photoperiods to optimize C. sativa floral biomass and cannabinoid content. To this end, we searched through ISI Web of Science for peer-reviewed publications of C. sativa that reported experimental photoperiod durations and results containing cannabinoid concentrations and/or floral biomass, then from 26 studies, we estimated the relationship between photoperiod and yield using quantile regression. Floral biomass was maximized when the long daylength photoperiod was minimized (i.e., 14 days), while THC and CBD potency was maximized under long day length photoperiod for ~42 and 49–50 days, respectively. Our work reveals a yield trade-off in C. sativa between cannabinoid concentration and floral biomass where more time spent under long-day lighting maximizes cannabinoid content and less time spent under long-day lighting maximizes floral biomass. Growers should carefully consider the length of long-day lighting exposure as it can be used as a tool to maximize desired yield outcomes.

Accuracy and Precision of Fixed and Random Effects in Meta-Analyses of Randomized Control Trials for Continuous Outcomes

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing correlation. As an alternative, we propose adopting a multivariate meta-regression approach that models independent group effect sizes and accounts for the dependency structure using robust variance estimation or three-level modeling. A comprehensive simulation study mimicking realistic conditions of meta-analyses in clinical and educational psychology suggested that the prevalent imputation approach works well for estimating the pooled effect but severely distorts the between-study heterogeneity. In contrast, the robust meta-regression approach resulted in largely unbiased fixed and random effects. Based on these results recommendations for meta-analytic practice and future meta-analytic developments are provided.

Information Asymmetries and Intra-Company Monitoring: an Empirical Analysis of Nonlinear Relationships Between Company Characteristics and the Size of the Internal Audit Function

The internal audit function (IAF) has become one of the main pillars of good corporate governance. Empirical findings show that the size of the IAF varies considerably across companies. This study analyzes the relationships between selected company characteristics as determinants of intra-company information asymmetries and the size of the IAF as an indicator of intra-company monitoring. We test these relationships by analyzing comprehensive survey data obtained from chief audit executives from 283 Austrian, German, and Swiss companies. Using a nonparametric regression approach, we identify significant nonlinear relationships between company characteristics and IAF size. The empirical analysis identifies threshold levels for several metric company characteristics, such as the number of employees and the number of subsidiaries, whose relationships with the size of the IAF change its intensity.