Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts' awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved...

The availability of panel data on the basis of micro data has become an indispensable component of the infrastructure of empirically oriented social scientists and economists. This is also a consequence of the fact that, for a panel survey, the quality of both content and methodological analyses...

This paper is the first study that analyzes the drivers of political protest using longitudinal data from a critical revolution that changed -at least temporarily- the political landscape in a transition country. We make use of a rich dataset consisting of panel data collected before and after...

Like other data quality dimensions, the concept of accuracy is often adopted to characterise a particular data set. However, its common specification basically refers to statistical properties of estimators, which can hardly be proved by means of a single survey at hand. This ambiguity can be...

The definition and operationalization of wealth information in population surveys and the corresponding microdata requires a wide range of more or less normative assumptions. However, the decisions made in both the pre- and post-data-collection stage may interfere consid-erably with the...

This article proposes an estimation approach for panel models with mixed continuous and ordered categorical outcomes based on generalized estimating equations for the mean and pseudo-score equations for the covariance parameters. A numerical study suggests that efficiency can be gained as...

This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when only discrete observations are available. The estimation is carried out using a tuned MCMC method, in particular a blocked Metropolis-Hastings algorithm, by introducing auxiliary points and...

This paper studies in some detail a class of high frequency based volatility (HEAVY) models.  These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data.  Our analysis identifies that the models have momentum and mean...

This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility models and counting process models. The diffusions can potentially be non-stationary. Although our...