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Course Code: 
EPP 7203
Course Credit Units: 
Semester 2
Year of Study: 
Year 1
Undergraduate or Graduate Level: 
Graduate Level
Course Description & Objectives: 

Course Description:This is an entry-level graduate econometrics course, focusing mainly on cross-sectional techniques and to a lesser extent on time-series techniques. It is entry-level in the sense that you are not presumed to have any prior acquaintance with econometrics (although you are assumed to have the required statistical and computing background; you are also assumed to have had coursework in linear algebra and calculus including some optimization). Though entry level, this is very much a graduate course, which among other things, means that rigor and understanding of the techniques are very much emphasized as opposed to learning cookbook methods. It attempts to serve two types of audiences. For those who wish to pursue applied data analysis in the real world, it presents a wide array of problem instances and tools appropriate for those instances. It will expose you to a tool, show you why it works (at least in most cases) and ask you to apply the tool to solve similar problems with new datasets. The course also serves as a stepping stone for those interested in knowing the field more intimately, which it does by introducing them to a fair amount of theory and by giving them a tour of a small selection of classic and contemporary papers written in this area.                                                                                                                                                                                                                                                        Objectives :This course aims to broaden your knowledge and extend your understanding of econometrics. By the end of the course you should be able to:

  1. Make progress with qualitative regressors, dummy variables and the identification and estimation of simultaneous econometric models;
  2. Show how lags and expectations can be incorporated in dynamic models; and
  3. Forecast with both econometric and time series models.


Learning Outcomes: 
  • Demonstrate a broad and deep knowledge of advanced core areas of econometrics;
  • Apply core advanced econometric theory and quantitative methods to applied topics;
  • Show understanding of advanced analytical methods, both theory- and model based;
  • Show understanding of relevant mathematical and statistical techniques;
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