## Courses NonDegree Display 2020-2021

Course Description To PDF
Course title Econometric Methods I
Course code EBC2111
ECTS credits 6,5
Period
Period Start End Mon Tue Wed Thu Fri
5 12-4-2021 28-5-2021 X X
Coordinator Alain Hecq
Language of instruction English
Goals
Students will have a good knowledge of econometric methods. They will have the skills to apply these methods to a set of economic data.
Description
PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. THE INFORMATION PROVIDED HERE IS BASED ON THE COURSE SETUP PRIOR TO THE CORONAVIRUS CRISIS. AS A CONSEQUENCE OF THE CRISIS, COURSE COORDINATORS MAY BE FORCED TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS. "ECONOMETRIC METHODS I" IS THE NEW TITLE FOR THE COURSE PREVIOUSLY LABELLED "ECONOMETRIC METHODS".

This course is part of the programme for second-year econometrics students. The challenge of econometrics is to answer the question, what everyday reality has to tell about economic theories. Here, everyday reality takes the form of numerical observations or 'data', while economic theories are translated into a formal statistical 'model' with corresponding hypotheses. In order to extract as much information as possible out of the former concerning the latter, an appeal is made to statistical induction. These are the 'econometric methods' that are the subject of this course. They comprise mainly the estimation of the model parameters, the testing of the model hypotheses, and making (conditional) predictions with the model. We will study the most frequently used statistical methods and techniques in the first place for the classical linear model, but we mainly focus of the matrix notations of usual linear estimators and test statistics (e.g., OLS, OLS, the t-tests, F-test). Those estimators will be implemented during the tutorial meetings using the software packages R and Eviews. Further some important assumptions will be relaxed and alternative estimators (GLS, SURE) will be investigated in the presence of autocorrelation and heteroskedasticity. This course also emphasize dynamic models and time series econometrics (ARMA, VAR, cointegration, unit root, VECM, ...). Applied works (R, Eviews) will be carried out during tutorial meetings. The course Econometrics Methods II in the programme for the third-year econometrics students, covers issues that we do not do in this course (IV, GMM, ML, ...).
Literature
Greene, W. (2012), Econometric Analysis, 7th ed., Pearson.
Prerequisites
A first course in econometrics (see, e.g. Empirical Econometrics). Exchange students should have advanced knowledge of: 1) Mathematical statistics, 2) probability theory, 3) matrix algebra, 4) introduction to quantitative methods with an emphasis to the linear model