Aplicaciones Organizacionales de TREC en el modelo RET

Las aplicaciones organizacionales de modelos psicológicos son una realidad frecuente en la práctica profesional. Sin embargo es frecuente que los soportes teóricos no sean elaborados de forma expresiva, debido al pragmatismo del entorno organizacional. Esta situación es visible al considerar el lenguaje, la estructura y el soporte teórico recogido en las publicaciones de psicólogos dirigidos al público de las empresas (el management). No obstante, las propuestas teóricas psicológicas en el campo del desarrollo, el desarrollo humano, la respuesta emocional, el aprendizaje en adultos y la cognición entre otros soportan modelos aplicados y desarrollos pragmáticos específicos. Así ocurre en el caso de TREC, que reconoce como los obstáculos emocionales relacionados con sobredemandas influyen negativamente en la comunicación y los vínculos que soportan el liderazgo en entorno organizacional. Diferentes aportes desde la inteligencia emocional permitirán entender y conectar la relación de estas teorías.

Biased technological change, human capital and factor shares

We propose a one-good model where technological change is factor saving and costly. We consider a production function with two reproducible factors: physical capital and human capital, and one not reproducible factor. The main predictions of the model are the following: (a) The elasticity of output with respect to the reproducible factors depends on the factor abundance of the economies. (b) The income share of reproducible factors increases with the stage of development. (c) Depending on the initial conditions, in some economies the production function converges to AK, while in other economies long-run growth is zero. (d) The share of human factors (raw labor and human capital) converges to a positive number lower than one. Along the transition it may decrease, increase or remain constant.

Confidence sets for asset correlations in portfolio credit risk

Asset correlations are of critical importance in quantifying portfolio credit risk and economic capitalin financial institutions. Estimation of asset correlation with rating transition data has focusedon the point estimation of the correlation without giving any consideration to the uncertaintyaround these point estimates. In this article we use Bayesian methods to estimate a dynamicfactor model for default risk using rating data (McNeil et al., 2005; McNeil and Wendin, 2007).Bayesian methods allow us to formally incorporate human judgement in the estimation of assetcorrelation, through the prior distribution and fully characterize a confidence set for the correlations.Results indicate: i) a two factor model rather than the one factor model, as proposed bythe Basel II framework, better represents the historical default data. ii) importance of unobservedfactors in this type of models is reinforced and point out that the levels of the implied asset correlationscritically depend on the latent state variable used to capture the dynamics of default,as well as other assumptions on the statistical model. iii) the posterior distributions of the assetcorrelations show that the Basel recommended bounds, for this parameter, undermine the levelof systemic risk.