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(This article was first published on <strong><a href="http://davegiles.blogspot.com/2016/05/graduate-econometrics-exam.html"> Econometrics Beat: Dave Giles' Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="http://www.r-bloggers.com/">R-bloggers)</a> </div>
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<div>Occasionally readers ask about the exams that I set in my graduate econometrics courses.</div>
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<div>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.</div>
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<div>This year most of the course was devoted to maximum likelihood, and Bayesian methods in econometrics.</div>
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<div>The mid-term test covered the first of these two thematic topics, while the final exam was devoted largely to Bayesian inference.</div>
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<div>You can find the mid-term test <a target="_blank" href="http://web.uvic.ca/~dgiles/blog/midterm_16.pdf">here</a>. The final exam question paper is <a target="_blank" href="http://web.uvic.ca/~dgiles/blog/Final_16.pdf">here</a>; and the associated R code is <a target="_blank" href="http://web.uvic.ca/~dgiles/blog/2016%20final.R">here</a>.</div>
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<p><center>© 2016, David E. Giles </center></div>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://davegiles.blogspot.com/2016/05/graduate-econometrics-exam.html"> Econometrics Beat: Dave Giles' Blog</a></strong>.</div>
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