The R-package phtt: Panel Data Analysis with Heterogeneous Time Trends

The R-package phtt provides estimation procedures for panel data with large dimensions n, T, and general forms of unobservable heterogeneous effects. Particularly, the estimation procedures are those of Bai (2009) and Kneip, Sickles, and Song (2012), which complement one another very well: both models assume the unobservable heterogeneous effects to have a factor structure. Kneip et al. (2012) considers the case in which the time varying common factors have relatively smooth patterns including strongly positive auto-correlated stationary as well as non-stationary factors, whereas the method of Bai (2009) focuses on stochastic bounded factors such as ARMA processes. Additionally, the phtt package provides a wide range of dimensionality criteria in order to estimate the number of the unobserved factors simultaneously with the remaining model parameters

Using the Gene Ontology to Annotate Biomedical Journal Articles

We are creating a gold-standard corpus of manually annotated full-text biomedical journal articles toward natural-language-processing applications. Central to this is our use of entire ontologies of the Open Biomedical Ontologies initiative as well as other terminologies as term sources, in contrast to most other such annotation projects, which have used small, ad hoc schemas. In addition to the standard difficulties in such annotation projects, each of the terminologies we have used has idiosyncrasies and ambiguities that present further challenges to consistent, high-quality annotation of these articles. In this paper we present and discuss the most salient of these with regard to the Gene Ontology that we have encountered and addressed in our annotation guidelines and training. The utility of these guidelines can be seen in the high and still-increasing interannotator-agreement statistics that we continually monitor.
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Panel Data Models with Unobserved Multiple Time- Varying Effects to Estimate Risk Premium of Corporate Bonds

We use a panel cointegration model with multiple time- varying individual effects to control for the missing factors in the credit spread puzzle. Our model specification enables as to capture the unobserved dynamics of the systematic risk premia in the bond market. In order to estimate the dimensionality of the hidden risk factors jointly with the model parameters, we rely on a modified version of the iterated least squares method proposed by Bai, Kao, and Ng (2009). Our result confirms the presence of four common risk components affecting the U.S. corporate bonds during the period between September 2006 and March 2008. However, one single risk factor is sufficient to describe the data for all time periods prior to mid July 2007 when the subprime crisis was detected in the financial market. The dimensionality of the unobserved risk components therefore seems to reflect the degree of difficulty to diversify the individual bond risks.Panel Data Model; Factor Analysis; Credit Spread; Systematic Risk Premium;

Panel Data Models with Unobserved Multiple Time - Varying Effects to Estimate Risk Premium of Corporate Bonds

We use a panel cointegration model with multiple time- varying individual effects to control for the enigmatic missing factors in the credit spread puzzle. Our model specification enables as to capture the unobserved dynamics of the systematic risk premia in the bond market. In order to estimate the dimensionality of the hidden risk factors jointly with the model parameters, we rely on a modified version of the iterated least squares method proposed by Bai, Kao, and Ng (2009). Our result confirms the presence of four common risk components affecting the U.S. corporate bonds during the period between September 2006 and March 2008. However, one single risk factor is sufficient to describe the data for all time periods prior to mid July 2007 when the subprime crisis was detected in the financial market. The dimensionality of the unobserved risk components therefore seems to reflect the degree of difficulty to diversify the individual bond risks.Corporate Bond; Credit Spread; Systematic Risk Premium; Panel; Data Model with Interactive Fixed Effects; Factor Analysis; Dimensionality Criteria; Panel Cointegration

Decreto de Nueva Planta: cambios en la relación Iglesia-Estado

El Decret de Nova Planta va comportar una sèrie de canvis de major o menor importància. El decret va afectar també les relacions de la nova dinastia amb l’Església catòlica i les seves institucions. Va afectar de manera evident les relacions frontereres entre diòcesis i va establir els fonaments per al futur. En aquest sentit, la geografia eclesiàstica de la Ribagorça ha estat dominada per la relació entre els bisbats de Barbastre i de Lleida. PARAULES CLAU: Decret de Nova Planta, Ribagorça, Barbastre, Església catòlica, Estat, ConciliThe Decree of ‘Nueva Planta’ carried a series of changes of major or minor importance. The decree also affected the relations of the new dynasty with the Catholic Church and its institutions. That clearly affected the frontier relations between dioceses and and established the way for the future. In this respect, the ecclesiastic geography of the Ribagorza has been dominated by the relation between the bishoprics of Barbastro and Lleida. KEY WORDS: Decree of ‘Nueva Planta’, Ribagorza, Barbastro, Catholic Church, State, Council.El Decreto de Nueva Planta conllevó una serie de cambios de mayor o menor importancia. El decreto afectó también las relaciones de la nueva dinastía con la Iglesia católica y sus instituciones. Éste afectó de manera evidente las relaciones fronterizas entre diócesis y estableció las bases para el futuro. En este sentido, la geografía eclesiástica de la Ribagorza ha estado dominada por la relación entre los obispados de Barbastro y Lleida. PALABRAS CLAVE: Decreto de Nueva Planta, Ribagorza, Barbastro, Iglesia católica, Estado, Concili

Una memoria per a la promoció de l'Alt Urgell (1779)

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