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...

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...

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 standard panel data literature is moving from micro panels, where the cross-section dimension is large and the intertemporal sample size is small, to large panels, where both, the cross-section and the time dimension, are large. This thesis contributes to this new and growing area of panel...

Functional principal component analysis (FPCA) based on theKarhunen-Lo`eve decomposition has been successfully applied in manyapplications, mainly for one sample problems. In this paper we consider common functional principal components for two sample problems. Our research is motivated not only...

Functional data analysis (FDA) has become a popular technique in applied statistics.In particular, this methodology has received considerable attention in recent studiesin empirical finance. In this talk we discuss selected topics of functional principalcomponents analysis that are motivated by...

Our paper introduces a new estimation method for arbitrary temporal heterogeneityin panel data models. The paper provides a semiparametric method for estimatinggeneral patterns of cross-sectional specific time trends. The methods proposed in thepaper are related to principal component analysis...

This work deals with a generalization of the Total Least Squaresmethod in the context of the functional linear model. We first propose asmoothing splines estimator of the functional coefficient of the model withoutnoise in the covariates and we obtain an asymptotic result for this...