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EconometricsIISyllabus.pdf | 2018-03-07 09:55 | 96K | ||
OldExams/ | 2017-03-14 16:09 | - | ||
ProblemSets/ | 2018-05-08 10:06 | - | ||
README.html | 2018-05-11 12:34 | 7.3K | ||
Software/ | 2018-03-07 10:15 | - | ||
Topics/ | 2018-03-07 10:17 | - | ||
gretl-guide.pdf | 2018-04-28 20:21 | 1.9M | ||
NEWS:
Problem Set 4 is available. It is due Thursday 17 May.
Please go to http://www.uab.cat/web/study/teaching-quality/evaluation-and-satisfaction-surveys-1345738816998.html and respond the the surveys regarding your professor, and the class.
Prob. set 3: for a hint for problem 2, see http://www.mathematica-journal.com/2011/12/sampling-distribution-of-ml-estimators-cauchy-example/
Prob. set 3, question 3: please see the Software/Matlab/GMM directory, the HallGMM.m file, which now shows how to compute standard errors
Problem set 3, question 3, makes reference to the GRETL manual, but there are different versions of the GRETL manual available, with different numbering. You should look at the chapter on GMM, section " A real example: the Consumption Based Asset Pricing Model". For the 2018 version, this is section 24.6.
Problem Set 3 is available, due Mon. 07 May.
I have updated Econometrics.jl mentioned in step 3, below, to make things work with Windows. If you have had trouble, please download it again.
the class for 09 May has been moved to 08 May, 15:00-17:00.
problem sets can be signed by a maximum of 3 people. The should be turned in printed (unless Augustin authorizes otherwise), and with sufficient details regarding the computational parts to verify how the work was done.
For GMM, the notes here have some nice examples, see especially the parts related to the stochastic volatility model (sections 6.2.3, 6.3.13, and 6.4.6).
GENERAL INFORMATION:
My notes and example programs are at https://github.com/mcreel/Econometrics
Get the file "econometrics.pdf" This has the examples mostly in Julia.
If you prefer a version with Matlab/Octave examples, get release v1 instead.
To follow the class, it is sufficient to read the notes and a textbook, and do the problem sets, using Matlab, Stata, Julia, Octave, Ox, Python, R, or other software of your choice. The software you use should allow you to minimize functions with and without constraints, and to numerically differentiate functions. The assistant, Augustin Tapsoba, (tapsoba.augustin@yahoo.fr) will be able to help you with Matlab and Stata. When reading the notes, you should make sure to read the code of the examples and make sure that you understand it. You don't actually have to run it.
However, if you're interested in running the examples, it's not too hard to do:
1. install Julia v0.6.x on your computer. I recommend the pre-compiled version for your OS, from the section Julia (command line version).
2. gain some experience with Julia, so that basic usage and installing packages is not a mystery. For example, see https://github.com/PaulSoderlind/JuliaTutorial
3. install econometrics.jl following the instructions on the linked page
4. install the notes by cloning or downloading from here
5. (optional) You can get the Juno/Atom editor here: http://junolab.org/