Courses Master Display 2017-2018
|Course Description||To PDF|
|Course title||Empirical Analysis I|
Alain Hecq, Bram Foubert
For more information: email@example.com; firstname.lastname@example.org
|Language of instruction||English|
Empirical analysis refers to research in which one gains knowledge on the basis of observation or experience. In economics and business economics, the goal of empirical analysis very often is to model and quantify relationships between various events or phenomena. In this course, we will learn how econometrics, and in particular its mainstay regression analysis, enable us to analyze (business) economic relationships between a phenomenon (the dependent variable) and its drivers (the independent variables). We will focus on relationships that are linear (or linearizable) in the parameters, and where the dependent variable is a real number (as opposed to, e.g., a nominal outcome).
More precisely, we will learn how we can build valid econometric models and study how and under which conditions we can use the method of ordinary least squares (OLS) to calibrate these models. We will discuss the problems if these conditions are not met and address possible solutions. Specific attention will be dedicated to models for time series data.
Throughout the course, we do not stick to a purely theoretical discussion of the relevant concepts but apply our knowledge to real-life problems. On the basis of structured assignments with realistic data, we will conduct econometric analyses in the open-source programming environment R. As such, a secondary objective of this course is to introduce students to data analysis in R, a skill that will also be useful in other courses and in many research-oriented jobs.
The course consists of seven building blocks: i) introduction to R and recap on matrix algebra and basic statistical inference, ii) regression analysis and ordinary least squares, iii) heteroscedasticity, iv) dynamic models and autocorrelation, v) stationary time-series models, vi) non-stationary time-series models, and vii) endogeneity and systems of equations. Each building block consists of a lecture and a tutorial in which we discuss an assignment in R.
Wooldridge, Jeffrey M. (2009), Introductory Econometrics: A Modern Approach (4th ed.), South-Western Cengage Learning, or any more recent version.
• Knowledge of basic statistics
• Knowledge of elementary algebra and basic calculus
• Experience with a statistical package like SPSS
• Notions of matrix algebra
• Notions of regression analysis and ordinary least squares (OLS)
• Notions of programming and algorithmic thinking
|Teaching methods||PBL / Lecture / Assignment|
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|This course belongs to the following programmes / specialisations||