Maximum likelihood has proved to be a valuable tool for fitting the log-normal stochastic volatility model to financial returns time series. Using a sequential change of variable framework, we are able to cast more general stochastic volatility models into a form appropriate for importance...

The first chapter of my thesis (co-authored with David N. DeJong, Jean-Francois Richard and Roman Liesenfeld) develops a numerical procedure that facilitates efficient likelihood evaluation and filtering in applications involving non-linear and non-Gaussian state-space models. These tasks...

Limited dependent variable (LDV) panel data models pose substantial challenges in maximum likelihood estimation. The likelihood function in such models typically contains multivariate integrals that are often analytically intractable. To overcome such problem in a panel probit model with...

First chapter of my dissertation uses an EGARCH method and a Stochastic Volatility (SV) method which relies upon Markov Chain Monte Carlo (MCMC) framework based on Efficient Importance Sampling (EIS) to model inflation volatility of Turkey. The strength of SV model lies in its success in...