Structural Macroeconometrics (SMA11)

This is a hands-on course on building and estimating dynamic macroeconomic models (DSGE models). We limit ourselves on perturbation methods and (log-)linear approximations. We first study model solution techniques, then compute and compare model and data moments, and finally estimate the parameter distribution by Bayesian techniques. Other techniques are reviewed too. The course can be passed by a term paper where these methods are applied. The course exercises involve working with the term paper. Therefore, it is highly suggested that a participant is prepared to work with his/her own model.

Course (preliminary) syllabus.


Proposed lecture and exercise dates are
May 9, Mon, 11-13 and 14-16; (seminar room 1)
May 10, Tue, 10-12 and 13-15;  ; (seminar room 1);
                      Exercises: 16-18 (computer class 3rd floor)
May 11, Wed, 10-12 and 13-15;  (seminar room 3-4)
May 12, Thu, 10-12 and 13-15  (seminar room 3-4).  
May 13, Fri, 10-12;  (computer class 3rd floor)
May 23, Mon, 10-12; (computer class 3rd floor)
May 25, Wed, 10-12; (computer class 3rd floor)

Course material

My slides! (contains all the course material; unpolished!)  

Iris code that I used to compute the spectral density functions shown in the lecture.

Iris Cookbook is very useful to start learning Iris. There might be tiny changes in the command syntax after the new release of Iris. The new release is also much wider in terms of the models and methods.


You will work with your own model during the exercise classes. Currently, the idea is that I will start by reviewing coding issues of the exercise theme. After this I will be available to you in helping in practical problems.

Ex 1: We try to code our models to Dynare (I will also review Yada and Iris schemes). You should know your model well and do the following in advance:
I might be able to help you in the above as well.

Ex 2: We compute model moments and talk about data.
Ex 3: We study model and data moments that the D/Y/I produce. For the data moments, we rely mostly on Iris. Download the code.

Ex 4: We set priors and talk about Bayesian estimation:

Term paper

You should return the term paper by the end of November 2011. I am considering the following rules of the game:

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