Le Grand Syllabus 2017/2018
sha, Running Randomized Evaluations : A Practical Guide, 2013, Princeton University Press.
Semester : Spring Number of hours : 48 Language of tuition : English
focus on policy implications. There are two modules available : a beginners module for students new to econometrics and an advanced module for students with a more quantitative. The topics covered are mainly the same in each module but covered with a different level of complexity. Throughout the course some real data examples are studied and a comparison of the different econometric techniques presented is made. These are examples relevant for education and health public sectors, for social policies, and for monetary policies. As potential consequences of these analysis there are effects on ﬁscal policies, on citizens security and welfare, and on consumers', investors' and government's behavior. We consider different type of data that an econometrician has to be able to handle : cross-sectional, longitudinal and, only for the advanced course, time series. For each type the appropriate models are presented and discussed with a focus on interpretation of results and on estimation. For the advanced module derivation of some asymptotic results is also covered. The regression models considered are : linear, non-linear, panel data, limited dependent variable, instrumental variable, and auto-regressions (only for the advanced course). Basic concepts of probability and statistics will be covered during the ﬁrst weeks of the course. Each lecture is followed by an applied computer session based on Stata where students learn to implement the techniques presented. This course description is thus valid for both the Econometrics Lectures (OAEA2080) and the Applied Econometrics with Stata classes (KOUT2050/ KOUT2055). The main objectives of the course are to teach students to be able to : - Conduct empirical research, i.e. to interpret the information extracted from the data in a critical way ; - Identify economic policy problems that can beneﬁt of quantitative analysis ; - Identify the appropriate method of analysis depending on the data at hand ; - Run statistical tests of hypothesis on real world data and to interpret the results. Required reading : Stock, James H. and Mark W. Watson. Introduction to Econometrics. Pearson Education, 2012.
Teachers : Jean-Marc ROBIN (Professeur des université - Directeur département d'économie, Sciences Po). Prerequisite : Students should have a robust knowledge of probability and statistical theory, including maximum likelihood. Pedagogical Format : Lecture and tutorials Senior lecturers : Julien PASCAL (Phd Student, Sciences Po). Course Description : The course follows up on the course taught in the ﬁrst semester on probability and statistics. It introduces econometric techniques used in empirical economics with survey data. This means that it does not cover time series, which are taught in a separate course. Slides, datasets and STATA codes can be found at the address : https ://sites.google.com/site/ jmarcrobin/teaching/m1-epp----introduction-toeconometrics. Required reading : Jeffrey M. Wooldridge, Econometric Analysis of Cross Section and Panel Data.
Semester : Autumn Number of hours : 24 Language of tuition : English
Teachers : Marta VICARELLI (Post doctoral associate). Prerequisite : An introductory undergraduate module in calculus and in probability and statistics. Pedagogical Format : Lecture alone Course validation : One mid-term exam and one ﬁnal exam (combined for the lectures and Stata classes). Course Description : The course covers the basics aspects of econometric analysis of real data with a 1248