Search

Several commercial banks in the United States disappeared during the last decades due to failure or acquisition by another entity. From a survival analysis perspective, however, the high censoring rate suggests that some institutions are likely to be immune to failure and/or acquisition. In this study, we use a competing risks proportional-hazards cure model in order to measure the impact of bank-specific and macroeconomic variables on the probabilities of being susceptible to these events (i.e. incidence) and on the survival time of susceptible banks (i.e. latency). Moreover, we propose to model the incidence distribution using Generalized Extreme Value regression and compare the results with the ones obtained by the usual logistic regression model. The proposed methodology is evaluated by means of a simulation study and then applied to a dataset of more than 4000 United States commercial banks spanning the period 1993–2018.

Suppose we have a location-scale regression model where the location is the conditional mean and the scale is the conditional standard deviation; the response is possibly right-censored, the covariate is fully observed, and the error is independent of the covariate. We propose new goodness-of-fit testing procedures for the conditional mean and variance based on an integrated regression function technique which uses artificial data points. We obtain the weak convergence of the resulting processes and study their finite sample behavior via simulations. Finally, we analyze a data set about unemployment in Galicia.

From a survival analysis perspective, bank failure data are often characterized by small default rates and heavy censoring. This empirical evidence can be explained by the existence of a subpopulat...

This article examines possible pedagogical applications of reflective methods not only in "Evolutive Age", similar to those used in oriental discipline settings, in educational or other contexts. Through extensive bibliographic research focusing on theoretical sources and empirical data and culminating in an analysis of findings related to the reported psychosocial characteristics under study, the author highlights the importance of rediscovering such disciplines from a free modern perspective, without preconditioning, and with opportunities for a wide range of applications

A random walk analysis of pressure and sea-level fluctuations from daily data taken at Trieste during the period 1939-2002 is presented. It is shown that the associated time series display long-time autocorrelations up to time scales of several thousand of days, characterized by a Hurst exponent H≈0.70 for sea-level and H≈0.58 for pressure. The corresponding probability distribution functions depart from the Gaussian shape, displaying asymmetric tails. The series are found to be cross-correlated with a cross-correlation coefficient of about -0.76. © 2004 Elsevier B.V. All rights reserved.