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We investigate whether and how the shift from discretionary forward-looking provisioning to the restrictive incurred loss approach under International Financial Reporting Standards (IFRS) in the European Union (EU) affects the cross-country comparability and predictive ability of loan loss allowances. Given bank supervisors’ keen interest in comparable and adequate loan loss allowances, we also examine the role of supervisors in determining financial statement effects around IFRS adoption. We find that the application of the incurred loss approach has led to more comparable loan loss allowances. However, some differences persist in countries where supervisors were reluctant to enforce the incurred loss approach. Our results also suggest that the predictive ability of loan loss allowances improved following IFRS adoption. Finally, in supplemental analyses we document that increased comparability of loan loss allowances is associated with the cross-country convergence of the risk sensitivity of bank leverage indicating an improvement in the effectiveness of market discipline in the EU.
Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ω as a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions.
Dual-task paradigms encompass a broad range of approaches to measure cognitive load in instructional settings. As a common characteristic, an additional task is implemented alongside a learning task to capture the individual’s unengaged cognitive capacities during the learning process. Measures to determine these capacities are, for instance, reaction times and interval errors on the additional task, while the performance on the learning task is to be maintained. Opposite to retrospectively applied subjective ratings, the continuous assessment within a dual-task paradigm allows to simultaneously monitor changes in the performance related to previously defined tasks. Following the Cognitive Load Theory, these changes in performance correspond to cognitive changes related to the establishment of permanently existing knowledge structures. Yet the current state of research indicates a clear lack of standardization of dual-task paradigms over study settings and task procedures. Typically, dual-task designs are adapted uniquely for each study, albeit with some similarities across different settings and task procedures. These similarities range from the type of modality to the frequency used for the additional task. This results in a lack of validity and comparability between studies due to arbitrarily chosen patterns of frequency without a sound scientific base, potentially confounding variables, or undecided adaptation potentials for future studies. In this paper, the lack of validity and comparability between dual-task settings will be presented, the current taxonomies compared and the future steps for a better standardization and implementation discussed.