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

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

Essays on Large Panel Data Models

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 data treatments called "large panel data analysis''. My dissertation consists of three essays: In the first essay, a large panel data model with an omitted factor structure is considered. The role of the factors is to control for the issue of the unobserved time-varying heterogeneity effects. A parameter cascading strategy is proposed to enable efficient estimation of all model parameters when the number of factors is unknown a priori. In the second essay, further models that combine large panel data models with different versions of unobserved latent factors are discussed. Computation-related issues are solved and new specification tests are introduced to check whether or not these factors can be interpreted as classical additive fixed effects. In the third essay, a novel method for estimating panel models with multiple structural changes is proposed. The breaks are allowed to occur at unknown points in time and may affect the multivariate slope parameters individually. Asymptotic results are derived, Monte Carlo experiments are performed, and applications for highlighting these new methods are discussed

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 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