Determinants of bank efficiency: Evidence from a semi-parametric methodology

In this paper, we use a semi-parametric two-stage model to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank efficiency. This method, proposed by Simar and Wilson (2007), relaxes several deficiencies of previous two-stage analyses, which regress non-parametric estimates of bank efficiency on exogenous determinants. In particular, we propose a bootstrap procedure to be used in the second stage and we compare the results obtained to the equivalents of a Tobit model. We suggest that the Tobit regressions inaccurately provide insignificant estimates for the effect of bank size, industry concentration and economic investment on bank efficiency, a fact that illustrates the power of the new method. Since the effect of these determinants has been ambiguous in previous literature, this may be a desideratum for future research.Bank efficiency; semi-parametric models

Exploring the Nexus between Banking Sector Reform and Performance: Evidence from Newly Acceded EU Countries

The aim of this study is to examine the relationship between banking sector reform and bank performance – measured in terms of efficiency, total factor productivity growth and net interest margin – accounting for the effects through competition and bank risk-taking. To this end, we develop an empirical model of bank performance and draw on recent econometric advances to consistently estimate it. The model is applied to bank panel data from ten newly acceded EU countries. The results indicate that both banking sector reform and competition exert a positive impact on bank efficiency, while the effect of reform on total factor productivity growth is significant only toward the end of the reform process. Finally, the effect of capital and credit risk on bank performance is in most cases negative, while it seems that higher liquid assets reduce the efficiency and productivity of banks.Bank performance; Banking sector reform; Competition; Risk-taking

Determinants of bank efficiency: evidence from a semi-parametric mathodology

Purpose 13 This paper aims to analyze bank efficiency into a number of bank-specific, industryspecific and macroeconomic determinants. Design/methodology/approach 13 The authors follow a semi-parametric two-stage methodology, where productive efficiency is derived via a non-parametric technique in the first stage and then the scores obtained are linked to a series of determinants of bank efficiency, using a double bootstrapping procedure. Findings 13 Overall, it is found that the banking sectors of almost all the sample countries show a gradual improvement in their efficiency levels. The model used shows that a number of determinants like bank size, industry concentration and the investment environment have a positive impact on bank efficiency, which is not the case when standard Tobit models are employed. Research limitations/implications 13 The findings have important implications for the relevance of well-known hypotheses that refer to the performance of the banking sectors, like the structure conduct-performance and the efficient structure hypotheses. These implications are not necessarily verified when past conventional econometric methodologies are used. Practical implications 13 The paper offers new insights to policy makers, bank managers and practitioners on the relevance of a number of driving factors of bank efficiency that might help them to improve the performance of the banking system and enhance the quality of services provided. Originality/value 13 This is the first paper in the bank efficiency literature that employs a semiparametric two-stage model, which relaxes several deficiencies of previous two-stage empirical approaches thus, offering a solution to the many problematic features of standard censored regression

Gender, credit, and firm outcomes.

Small and micro-enterprises are usually majority-owned by entrepreneurs. Using a unique sample of loan applications from such firms, we study the role of owners’ gender in bank credit decisions and post-credit-decision firm outcomes. We find that, ceteris paribus, female entrepreneurs are more prudent loan applicants than are males because they are less likely to apply for credit or to default after loan origination. The relatively more aggressive behavior of male applicants pays off, however, in terms of higher average firm performance after loan origination

Space-by-time manifold representation of dynamic facial expressions for emotion categorization

Visual categorization is the brain computation that reduces high-dimensional information in the visual environment into a smaller set of meaningful categories. An important problem in visual neuroscience is to identify the visual information that the brain must represent and then use to categorize visual inputs. Here we introduce a new mathematical formalism—termed space-by-time manifold decomposition—that describes this information as a low-dimensional manifold separable in space and time. We use this decomposition to characterize the representations used by observers to categorize the six classic facial expressions of emotion (happy, surprise, fear, disgust, anger, and sad). By means of a Generative Face Grammar, we presented random dynamic facial movements on each experimental trial and used subjective human perception to identify the facial movements that correlate with each emotion category. When the random movements projected onto the categorization manifold region corresponding to one of the emotion categories, observers categorized the stimulus accordingly; otherwise they selected “other.” Using this information, we determined both the Action Unit and temporal components whose linear combinations lead to reliable categorization of each emotion. In a validation experiment, we confirmed the psychological validity of the resulting space-by-time manifold representation. Finally, we demonstrated the importance of temporal sequencing for accurate emotion categorization and identified the temporal dynamics of Action Unit components that cause typical confusions between specific emotions (e.g., fear and surprise) as well as those resolving these confusions

Determinants of bank efficiency: Evidence from a semi-parametric methodology

In this paper, we use a semi-parametric two-stage model to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank efficiency. This method, proposed by Simar and Wilson (2007), relaxes several deficiencies of previous two-stage analyses, which regress non-parametric estimates of bank efficiency on exogenous determinants. In particular, we propose a bootstrap procedure to be used in the second stage and we compare the results obtained to the equivalents of a Tobit model. We suggest that the Tobit regressions inaccurately provide insignificant estimates for the effect of bank size, industry concentration and economic investment on bank efficiency, a fact that illustrates the power of the new method. Since the effect of these determinants has been ambiguous in previous literature, this may be a desideratum for future research

Sur la solution numerique des systems creuses et linéaires émergents de la discretization volume finis des modeles 2D de type Boussinesq

This work supplements the realization and validation of a higher-order well balanced finite volume (FV) scheme developed for numerically simulating, on triangular meshes, weakly non-linear weakly dispersive water waves over varying bathymetries. The scheme has been recently presented by Kazolea et al. \textit{(Coastal Eng. 69:42-66, 2012 and J. Comp. Phys. 271:281-305, 2014)}. More precisely, we investigate and develop solution strategies for the sparse linear system that occurs during this FV discretisation of a set of Boussinesq-type equations on unstructured meshes. The resultant system of equations must be solved at each time step as to recover the actual velocity field of the flow. The system's coefficient matrix is sparse, un-symmetric and often ill-conditioned. Its characteristics are affected by physical quantities of the problem to be solved, such as the un-disturbed water depth and the mesh topology. This work investigates the application of different iterative techniques, with and without the usage of preconditioners and reordering, for the solution of this sparse linear system. Two different iterative methods, three preconditioning techniques, including different ILU factorizations and two different reordering techniques are implemented and discussed. An optimal strategy, in terms of computational efficiency and robustness, is proposed.Ce travail concerne la réalisation et la validation d’un schéma Volumes Finis d’ordre élevé pour la simulation des vagues en régime faiblement non-linéaire et faiblement dispersife sur bathymétries variables. Le schéma implémenté est celui proposé récemment par Kazolea et al. (Coastal Eng 69:. 42-66, 2012 et J. Phys Comp 271:.. 281-305, 2014). Plus précisément, nous étudions et développons des stratégies de solution pour le système linéaire creux qui se produit au cours de la discrétisation des équations de Boussinesq sur maillagesnon structurés. Le système d’équations résultant doit être résolu à chaque pas de temps pour récupérer la vitesse. La matrice du système est creuse, non symétrique et souvent mal conditionné. Ses caractéristiques sont affectées par des quantités physiques tels que la profondeur de l’ eau au repos et la topologie du maillage. Ce travail étudie l’ application de différentes techniques itératives, avec et sans l’ utilisation de pré conditionneurs et de ré-numérotation, pour la solution de ce système linéaire creux. Deux méthodes itératives différentes, troistechniques de pré conditionnement, y compris les différents factorisations ILU et deux techniques de ré ordonnancement différentes sont mises en œuvre et évaluées. Une stratégie optimale, en termes d’efficacité de calcul et de robustesse, est proposé

Persistence of cognitive impairment and its negative impact on psychosocial functioning in lithium-treated, euthymic bipolar patients: a 6-year follow-up study.

BACKGROUND: Previous cross-sectional studies report that cognitive impairment is associated with poor psychosocial functioning in euthymic bipolar patients. There is a lack of long-term studies to determine the course of cognitive impairment and its impact on functional outcome. Method A total of 54 subjects were assessed at baseline and 6 years later; 28 had DSM-IV TR bipolar I or II disorder (recruited, at baseline, from a Lithium Clinic Program) and 26 were healthy matched controls. They were all assessed with a cognitive battery tapping into the main cognitive domains (executive function, attention, processing speed, verbal memory and visual memory) twice over a 6-year follow-up period. All patients were euthymic (Hamilton Rating Scale for Depression score lower than 8 and Young mania rating scale score lower than 6) for at least 3 months before both evaluations. At the end of follow-up, psychosocial functioning was also evaluated by means of the Functioning Assessment Short Test. RESULTS: Repeated-measures multivariate analysis of covariance showed that there were main effects of group in the executive domain, in the inhibition domain, in the processing speed domain, and in the verbal memory domain (p<0.04). Among the clinical factors, only longer illness duration was significantly related to slow processing (p=0.01), whereas strong relationships were observed between impoverished cognition along time and poorer psychosocial functioning (p<0.05). CONCLUSIONS: Executive functioning, inhibition, processing speed and verbal memory were impaired in euthymic bipolar out-patients. Although cognitive deficits remained stable on average throughout the follow-up, they had enduring negative effects on psychosocial adaptation of patients

Identification of spatial-temporal muscle synergies from EMG epochs of various durations: a time-warped tensor decomposition

Extraction of muscle synergies from electromyography (EMG) recordings relies on the analysis of multi-trial muscle activation data. To identify the underlying modular structure, dimensionality reduction algorithms are usually applied to the EMG signals. This process requires a rigid alignment of muscle activity across trials that is typically achieved by the normalization of the length of each trial. However, this time-normalization ignores important temporal variability that is present on single trials as result of neuromechanical processes or task demands. To overcome this limitation, we propose a novel method that simultaneously aligns muscle activity data and extracts spatial and temporal muscle synergies. This approach relies on an unsupervised learning algorithm that extends our previously developed space-by-time decomposition to incorporate the identification of linear time warps for individual trials. We apply the proposed method to high-dimensional spatiotemporal EMG data recorded during performance of whole-body reaching movements and show that it identifies meaningful spatial and temporal structure in muscle activity despite differences in trial lengths. We suggest that this algorithm is a useful tool to identify muscle synergies in a variety of natural self-paced motor behaviors