We implement a long-horizon static and dynamic portfolio allocation involvinga risk-free and a risky asset. This model is calibrated at a quarterly frequencyfor ten European countries. We also use maximum-likelihood estimates andBayesian estimates to account for parameter uncertainty. We nd that...

In the social sciences, it is often useful to introduce latent variables and usestructural equation modeling to quantify relations among observable and latentvariables. Data gathered through surveys is frequently ordinal in nature, asLikert-scale type questions are used in most questionnaires....

Objective of this paper is to enhance the understanding of modelling jumpsand to analyse the model risk based on the jump component in electricity markets.We provide a common modelling framework that allows to incorporate the main jumppatterns observed in electricity spot prices and compare the...

Why should risk management systems account for parameter uncertainty? In order to answer this question, this paper lets an investor in a credit portfolio face non-diversifiable estimation-driven uncertainty about two parameters: probability of default and asset-return correlation. Bayesian...

We measure labor market frictions using a strategy that bridges design-based and structuralapproaches: estimating an equilibrium search model using reduced-form minimum wageelasticities identified from border discontinuities and fitted with Bayesian and LIML methods.We begin by providing the...

We use information from two prospective British birth cohort studies to explorethe antecedents of adult malaise, an indicator of incipient depression. Thesestudies include a wealth of information on childhood circumstances, behaviour,test scores and family background, measured several times...

This paper introduces an expected value estimator with expert knowledge to the robust estimation of sovereign rating transitions which are characterised by few observations. Ourestimates of default premia within Mexican, Colombian and Brazilian Eurobond yield spreads provide a better fit than...

Bayesian Model Averaging (BMA) has recently been discussed in the financial literature as an effective way to account for model uncertainty. In this paper we compare BMA to a new model uncertainty framework introduced by Yang (2004), called Aggregate Forecasting Through Exponential Reweighting,...

We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extendingit in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying...

We present an object-oriented software framework allowing to specify, solve, and estimate nonlinear dynamic general equilibrium (DSGE) models. The implemented solution methods for finding the unknown policy function are the standard linearization around the deterministic steady state, and a...