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...

The iterative least squares method for estimating panel models with unobservable factor structure is extended to cover the case where the number of factors is unknown a priori. The proposed estimation algorithm optimizes a penalized least squares objective function to estimate the factor...

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 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...

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...