This unit provides an introduction to modern computationally intensive algorithms, their implementation and application for carrying out statistical inference on econometric models. Students will learn modern programming techniques such as Monte Carlo simulation and parallel computing to solve econometric problems. The computational methods of inference include Bayesian approach, bootstrapping and other iterative algorithms for estimation of parameters in complex econometric models. Meanwhile, students will be able to acquire at least one statistical programming language.
1x2hr lecture/week, 1x1hr computer laboratory/week
1x2hr Final Exam (50%), 1x1500wd Computer Project (30%), 2x500wd Computer Assignment (20%)
ECMT2160 or ECMT2110