Maximum likelihood estimation (MLE) of stochastic differential equations (SDEs) is difficult because in general the transition density function of these processes is not known in closed form, and has to be approximated somehow. An approximation based on efficient importance sampling (EIS) is...

This paper deals with the estimation of continuous time stochastic volatility models of option pricing. We argue that option prices are much more informative about the parameters than asset prices. This is confirmed in a Monte Carlo experiment which compares two very simple strategies based on...

This paper exploits an Italian microeconomic data set to study retirement behaviour of women and men using survival analysis techniques. After a brief introduction to the Italian pension system and to the Bank of Italy survey, different specifications of a Cox proportional hazard model are...

This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most effcient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood...

This article deals with the estimation of continuous-time stochastic volatility models of option pricing. We argue that option prices are much more informative about the parameters than are asset prices. This is confirmed in a Monte Carlo experiment that compares two very simple strategies based...

An inference method, called latent backfitting, is proposed. This method appears well suited for econometric models where the structural relationships of interest define the observed endogenous variables as a known function of unobserved state variables and unknown parameters. This nonlinear...