Two maximum likelihood methods for estimating the parameters of stochastic differential equations (SDEs) from time-series data are proposed. The first is that of simulated maximum likelihood in which a nonparametric kernel is used to construct the transitional density of an SDE from a series of...

We propose a method for the simultaneous estimation of the drift and diffusion coefficients of stochastic differential equations (SDE) from panel data. The method involves matching the distribution of the experimental/field data with a panel of simulated data generated by a Monte Carlo...

Times-series data which are observed at irregular time intervals often arise in economics and the bio-sciences. Existing methods for modelling these data have focused on the discretisation of continuous processes. A method is proposed for fitting cyclical components to irregular time-series data...

This paper examines whether or not a discrete-time econometric test for nonlinearity in mean may be used in cases where the data are believed to be generated in continuous time. It is demonstrated that appropriate bootstrapping techniques are required to yield a test statistic with sensible...