ECON 7801 - Econometrics II

This course offers an in-depth treatment of advanced topics in time series analysis, focusing on Bayesian inference. The course will equip students with the necessary knowledge to undertake econometric analysis of the type commonly associated with modern macroeconomic research. Topics include autoregressive AR models, models with a general covariance matrix (such as models with t-errors and moving average MA errors), Bayesian model comparison methods, Markov chain Monte Carlo (MCMC) methods (such as Gibbs sampling and Metropolis-Hastings algorithms), mixture models, unobserved components models, stochastic volatility models, and Vector Autoregressions. Emphasis is on hands-on implementation of the models and methods covered in the course, implemented using R and Matlab.