MacroEconometricsS2024 Macroeconometrics

Spring 2024       Instructor: Bent E. Sorensen

No class: 1/24/24. Make-up class: TBA if needed. On-line class: TBA if needed.

Student Presentations:

2/26: Santiago
2/28: Swati
3/4 Sebin
3/11-13 Spring Break
3/18: Sterre
3/20: Javier
4/1: Ali
4/3: Hanieh
4/8: Alexis
4/15: Arsalan

Syllabus

To pass the class: Occasional homeworks in the first half of semester, maybe a short mid-term, 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. You can also suggest topics to be covered.

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. You can use Matlab code, but you can use whatever code you want. (R is becoming very popular...I have not used it in my own papers, so I cannot give direct help.) so we can try out the various methodsl I will listen to student preferences, if you have any, but I will start with Unit Roots and then co-integration or 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. (This was covered very briefly in Econometrics II, but maybe that was enough, let me know.)
  • Dynamic Factor Models Stock and Watson (again) survey
  • 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.
Notes:

Very short note on Principal Components (last update 2/20/22) .

Notes on Kalman Filter (last update 2/12/24) .

Notes on Unit Roots (last update 2/2/22) .

Notes on Cointegration (last update 2/10/22) .

Notes on Dynamic Factor Models (2/21/22) .

Notes on ARCH and Volatility Models (3/2/22) .

Homework #  ;Due

Homework 1                    Wed Jan 24

Homework 2      Gauss Johansen code  Matlab Johansen code  Wed Feb 6

Homework 3      Matlab code Matlab loops    Wed Feb 28