Estimation of State Space Models using Particle Filters – applications to Economics and Finance

In recent years, general state space models have been proven to be extremely useful in modelling wide range of economic and financial time series. Subsequently, particle filters, a computational simulation based method along with its related techniques had burst into our spectrum and fill our expectation of estimating general state space models. However, particle methods can be computationally intensive, as well as possibly requiring stringent restrictions on the parameters space to achieve timely convergence. In this thesis, I propose several improvements to particle methods on different aspects. A list of the improvements are: general computational time reduction in particle filters, modified particle smoothing algorithm, more accurate parameter and state variable estimation through the utilizations of Modified Entropy particle filter, and apply novel general state space model estimation method to real economic and financial time series.