The objectives of this course are twofold: to equip the students with proper
tools for advanced empirical work and to lay the foundation of advanced econometric theory. To meet the goals, I give the lectures in five parts. The first part includes the introduction of stochastic process and a summary review of testing causality within cointegrated system assuming that students have already been exposed to the topics on unit root and cointegration analysis in Econometric I and II. Then comes the second part concerning asymptotic econometric theorems.

The next three parts cover three promising topics useful for economic empirical research, namely State Space model, survival analysis and hierarchial. Estimating potential output and output gap is an important application of State Space model and Kalman filter. As for survival analysis, I focus on the application on credit risk modeling. Lastly, I shall cover hierarchial models from the Bayesian perspectives. For many economics students, this might be the first encounter of Bayesian econometrics and hierarchial models. Yet, I hope to convince you the worthiness
of learning the new tricks.

In additional to econometric analysis, I also emphasize computational aspects
of these complicated econometric techniques. R, is the statistical packages used in this course.