Courses Bachelor Display 2019-2020
|Course Description||To PDF|
|Course title||Econometric Methods II|
Denis de Crombrugghe
For more information: firstname.lastname@example.org
|Language of instruction||English|
(1) Thorough understanding of standard econometric models and methods for the analysis of independent data; independent data are typically cross-sectional, as opposed to time series which are sequential and generally serially dependent.
(2) Additionally, some practical experience with the application of the methods, the interpretation of the models, and the evaluation of inferences.
(3) In particular, providing background and warming up for students about to write a Bachelor thesis on an empirical topic.
The course is designed as a follow-up to the second-year course Econometric Methods I (EBC2111), reviewing known methods somewhat more formally before introducing the new ones. The following topics will be covered.
(1) The Normal regression model and Maximum Likelihood (ML)
(2) Endogeneity and Instrumental Variable (IV) methods
(3) Generalised Method of Moments (GMM)
(4) Discrete choice models (LPM, logit, probit etc.)
(5) Censoring and selection (tobit, heckit)
(6) Linear equation systems (SURE, SEM)
(7) Panel data models (POLS, FE, RE, FD ...).
These topics will be treated at a fairly rigorous level, starting from abstract assumptions about a multivariate world described in terms of vectors and matrices.
Hansen, Bruce E. (2018): Econometrics, University of Wisconsin webpage http://www.ssc.wisc.edu/~bhansen/econometrics/
Greene W.H. (2008): Econometric Analysis, 7th edition, Pearson Prentice Hall.
Davidson R. & J.G. MacKinnon (2004): Econometric Theory and Methods, Oxford University Press.
Wooldridge J.M. (2010): Econometric Analysis of Cross-Section and Panel Data, 2nd edition, MIT Press, Cambridge, MA. (First half).
Cameron A.C. & P.K. Trivedi (2005): Microeconometrics, Cambridge University Press. (First half).
Linear algebra, mathematical statistics (EBC2107), Econometric Methods I (EBC2111) or the equivalent.
Familiarity with statistical software like Stata or EViews and R.
|Teaching methods||PBL / Presentation / Lecture / Assignment / Groupwork / Skills|
|Assessment methods||Final Paper / Attendance / Participation / Written Exam / Assignment|
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