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...

We show how Data Envelopment Analysis (DEA) can handle missing data. When blank data entries are coded by appropriate dummy values, the DEA model automatically excludes the missing data from the analysis. We extend this result to weight-restricted DEA models by presenting a simple modification...

Empirical studies of the role of non-cognitive skills in driving economic behavior often rely heavily on the assumption that these skills are stable over the relevant time frame. We analyze the change in a specific non-cognitive skill, i.e. locus of control, in order to directly assess the...