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Despite the single currency, yields on government bonds in the Euro Area deviate substantially from German bond yields. These bond spreads are usually attributed to differing default and liquidity risks. The empirical literature documents that evaluation of these risks is subject to time variation in global factors approximated by US corporate bond spreads or short term interest rates. Within this paper time variation is modeled via latent processes instead of proxy variables. The findings suggest that default risk measured via expected debt to gross national product ratio explains a major part of the differences in band yields in the Euro area between 2003 and the unfolding of the financial crises. During the financial crises both risks gain importance in explaining bond spreads, with increased relative importance of liquidity risks compared to default risks or their market perception.

Despite the single currency, yields on government bonds in the Euro Area deviate from German bond yields. These bond spreads are usually attributed to differing default and liquidity risks. Recent research points out that time-varying global factors, approximated by risk measures or short term interest rates, play an important role for the evaluation of theses risks. In this paper, instead of proxy variables latent processes are assumed to model the aforementioned time variation. We find, that default risks measured via expected debt-to-GDP ratio explain a good stake of the variation of bond spreads in the Euro area at least between 2003 and the take-off of the financial crisis. During the financial crisis default risks or rather their evaluation increased but lost relative importance compared to liquidity risks.

ABSTRACTThis paper analyses the costs of housing crises in terms of GDP growth and the economic conditions under which crises are particularly costly. Housing crises are often followed by recessions that are longer than other recessions. According to empirical estimates, a housing crisis reduces the GDP growth rate in the following year on average by two percentage points and has still a considerable negative impact in the second year. One important channel through which the effect of housing crises is passed on seems to be the banking sector. In addition, our results suggest that negative wealth effects possibly cause further reductions in GDP growth.

The National Educational Panel Study (NEPS) provides data on the development of compe-tencies across the whole life span to educational researchers and politicians. Plausible values asa measure of individual competence are estimated by explicitly including background variablescapturing individual characteristics into the corresponding Item Response Theory (IRT) models.Despite tremendous efforts in field work, missing values in the background variables can occur.Adequate estimation routines are needed to reflect the uncertainty stemming from missing valuesin the background variables in the estimation of plausible values. To achieve this, we proposeto adapt an estimation strategy based on Markov Chain Monte Carlo (MCMC) techniques thatsimultaneously addresses missing values in background variables in the estimation of plausiblevalues for the competence scores. The resulting hybrid sampling scheme establishes a one-stepapproach for the estimation of plausible values using IRT models that incorporate backgroundvariables with missing values. In a simulation study allowing to control the mechanism causingmissing values, we evaluate the validity of our approach with respect to statistical accuracy. Theresults show that the proposed approach is capable to recover the true regression parametersdescribing the relationship between latent competence scores and background variables. Theapproach is illustrated on an example using data from the NEPS on mathematical competenciesof fifth grade students.

Large-scale surveys typically exhibit data structures characterized by rich mutual dependencies between surveyed variables and individual-specific skip patterns. Despite high efforts in fieldwork and questionnaire design, missing values inevitably occur. One approach for handling missing values is to provide multiply imputed data sets, thus enhancing the analytical potential of the surveyed data. To preserve possible nonlinear relationships among variables and incorporate skip patterns that make the full conditional distributions individual specific, we adapt a full conditional multiple imputation approach based on sequential classification and regression trees. Individual-specific skip patterns and constraints are handled within imputation in a way ensuring the consistency of the sequence of full conditional distributions. The suggested approach is illustrated in the context of income imputation in the adult cohort of the National Educational Panel Study.