Importance sampling is used in many aspects of modern econometrics to approximate unsolvable integrals. Its reliable use requires the sampler to possess a variance, for this guarantees a square root speed of convergence and asymptotic normality of the estimator of the integral. However, this...

Stochastic volatility models present a natural way of working with time-varying volatility. However the difficulty involved in estimating these types of models has prevented their wide-spread use in empirical applications. In this paper we exploit Gibbs sampling to provide a likelihood framework...

This paper deals with the estimation and testing of conditional duration models by looking at the density and baseline hazard functions. More precisely, we focus on the distance between the aprametric density (or hazard rate) function implied by the duration process and its non-parametric estimate.

In this paper we formalize budgetary stabilizers as a set of simple policy rules, and assess their operation in an uncertain environment by performing stochastic simulations in a forward-looking multi-country macroeconomic model, NiGEM, comprising individual blocks for 10 Euroland economies.