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...

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...

Weighted low-rank approximation (WLRA), a dimensionality reduction technique for data analysis, has been successfully used in several applications, such as in collaborative filtering to design recommender systems or in computer vision to recover structure from motion. In this paper, we study the...

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...

Nowcasting regards the inference on the present realization of random variables, on the basis of information available until a recent past. This paper proposes a modelling strategy aimed at a best use of the data for nowcasting based on panel data with severe deficiencies, namely short times...

By using a semiparametric specification, we examine the impact of urban concentration in economic growth on different groups of countries that we classify according to a geographical criterion or according to their level of development. Facing a significant proportion of missing data, we handle...

A new nonparametric estimator of production frontiers is defined and studied when the data set of production units is contaminated by measurement error. The measurement error is assumed to be an additive normal random variable on the input variable, but its variance is unknown. The estimator is...

The reduction theory of David F. Hendry provides a comprehensive probabilistic framework for the analysis and classification of the reductions associated with empirical econometric models. However, it is unable to provide an analysis on the sameunderlying probability space of the first reduction...

In this paper we exploit the specific structure of the Euler equation and develop two alternative GMM estimators that deal explicitly with measurement error. The first estimator assumes that the measurement error is lognormally distributed. The second estimator drops the distributional...