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...

This paper is concerned with the estimation of stochastic differential equations when only discrete observations are available. It primarily focuses on deriving a closed form solution for the one-step ahead conditional transition density using the Milstein scheme. This higher order Taylor...

This paper studies uniqueness of equilibrium in symmetric 2 x 2 bayesian games.  It shows that if signals are highly but not perfectly dependent then players play their risk-dominant actions for all but a vanishing set of signal realizations.  In contrast to the global games literature, noise...