Statistical and econometric modelling enhances our understanding of the behaviour of individuals, firms and other economic agents. This may simply involve the quantification of relationships between important driving forces within the economy but more fundamentally statistical and econometric models can provide evidence that will help discriminate between alternative views of how economic agents behave. Over the last 20 years computing power has increased dramatically and led to the development of statistical and econometric methods that utilize this power to more directly model behavioural relationships. The purpose of this course is to introduce computationally intensive statistical and econometric methods to carry out inference - estimation, hypothesis testing, confidence intervals and prediction - for complex models used in the Social Sciences. The course will provide an introduction to Bayesian inference using Markov Chain Monte Carlo simulation, simulated methods of moments estimation, and bootstrap methods. Examples and case studies of the applications of the methodology will also be provided. Actual applications will be drawn from economics, finance and marketing, but similar methods can be applied to statistical problems in the physical sciences and engineering.