MacroEconometricsS2022

MacroEconometricsS2022 Macroeconometrics
Spring 2022 Instructor: Bent E. Sorensen

No class: TBA if needed. Make-up class: TBA if needed.

To pass the class: Occasional homeworks, one (or two?) presentations, a final project. You can do a replication, a Monte Carlo, a technical trying-out of a recent method, a less technical, maybe data-intensive project, something related to your 2nd or 3rd year paper. You can do a project with another student. To some extent you can find your own way for the project part of the class.

A good place to look for summaries of econometric topics is the list of NBER lectures. NBER econometrics lectures

One good way to get an idea for your class project is too look at recent issue of the American Economic Review or American Economic Journal: Macroeconomics. These journals request authors to post their data (if feasible). Below is a list of topics. I assume you will write Matlab code, or find it, or adapt Gauss code of mine, so we can try out the various methodsl I will listen to student preferences, if you have any, but I will start with SVARS.

Vector Autoregressive Models (VARs) and Structural VARs, see Stock and Watson JEP survey on VARs (or see the NBER lecture on this and Local Projections)

Teaching notes on SVARs
Teaching notes on Granger Causality (a small topic)
GMM/Indirect inference. GMM can be uses in the any context, but sometimes it is the natural tool to use. Especially, for non-linear macro models.
Dynamic Factor Models Stock and Watson (again) survey (I will find or write some material on static factor models to start from, if we do this material)
Unit roots and co-integration (I have written notes on unit root testing and Johansen cointegration that I will post)
Kalman Filter
Regime Switching Models (Popularized by Hamilton, so Chapter 22 in his Time Series Analysis may be the place to start)
Bayesian methods (applications to dynamic models, VARs)
More micro: Dynamic models of panel data, Duration models,
Models of volatility.