Structural Macroeconometrics (SMA11)
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 materialMy slides! (contains all the course material; unpolished!)
Iris code that I used to compute the spectral density functions shown in the lecture.
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.
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
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
I might be able to help you in the above as well.
- Equilibrium conditions (these include decision rules, budget constraints, market equilibrium, etc)
- Stationarize your model
- Calculate deterministic steady state and try to solve it
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.
- You should have your data organized into matrix form at Excel/etc sheets.
Ex 4: We set priors and talk about Bayesian estimation:
Term paperYou should return the term paper by the end of November 2011. I am considering the following rules of the game:
- My markings will be based on the usual scale.
should provide theoretical model (not necessarily original); report the
most important data and model moments and estimate the model
parameters; report MCMC diagnostics, etc.
- Those, who are working with purely
nonlinear models and are not going to estimate the model parameter
values, I ask for data and model moments with particular and thorough emphasis on
- Other rules will follow.
Back to my website http://www.ripatti.net