Monday, May 16, 2016

Graduate Econometrics Exam

Occasionally readers ask about the exams that I set in my graduate econometrics courses.

The elective graduate econometrics course that I taught this past semester was one titled "Themes in Econometrics". The topics that are covered vary from year to year. However, as the title suggests, the course focuses on broad themes that arise in econometrics. Examples might include maximum likelihood estimation and the associated testing strategies;instrumental variables/GMM estimation; simulation methods; nonparametric inference; and Bayesian inference.

This year most of the course was devoted to maximum likelihood, and Bayesian methods in econometrics.

The mid-term test covered the first of these two thematic topics, while the final exam was devoted largely to Bayesian inference.

You can find the mid-term test here. The final exam question paper is here; and the associated R code is here.


© 2016, David E. Giles

7 comments:

  1. uff, a typical exam with a lot of theoretical topics that students will not use ever.
    Good job|

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    1. Thanks for the feedback. You're entitled to your opinion, of course, but it would mean more if it was put in context. If you bothered to check our dept. website you'll see that at the grad. level we offer a general (theory/applied) econometrics course for all incoming students. Apart from the theory elective mentioned in this post, we offer three applied econometrics electives - a general one; one on applied time-series econometrics; and one on applied microeconometrics. And BTW, the students DO use the topics from the "Themes" course - in their other courses, and elsewhere. Sorry if that sounds defensive, but our students get a pretty balanced "diet" of econometrics.

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    2. I'm not the original commenter, but I just wanted to say that that was a remarkably restrained and informative response to a quite rude comment. Kudoz to you, Prof Giles.

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  2. Are you able list the resources prescribed for this course?

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    1. Thom - we use Greene as a background text (partly because the students have it already from the previous compulsory "intro" course). When I'm teaching this course I have my own detailed slides for the bulk of the material. There are weekly computing labs., where the students get to apply the material to real data. I use both EViews and R throughout the course - mainly EViews for the MLE part (and IV/GMM if that's included), but almost exclusively R for the Bayesian material.

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    2. Thom - I should add that the assessment also includes several assignments, and an empirical project worth 20% where the students have to apply some of the techniques learned in the course to a problem of their choice.

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