Syllabus
Research Track - AQME Certificate courses (Graduate Program in Economy) - 9 ECTS
- Data Management and Programming
Data Management and Programming
Ects : 3
Lecturer :
Total hours : 36
Overview :
This course provides an introduction to programming and to data management, with a data- oriented point of view. The course contains two parts. The data management part introduces the data life cycle in data oriented projects from data collection to data exploration. While the main focus of the course is tabular data, it contains also an introduction to entity-relationship models and to relational databases. The programming part of the course introduces the fundamental aspects of imperative programming and the use of the main R data structures. The two aspects of the course are tightly integrated: each aspect of data management is illustrated by adapted programming constructs and uses specific data structures from R. In addition, an introduction to reproducible research is provided, using active documents (in quarto) and git.
Coefficient : 1 (Pour le M1 Affaires Internationales et Développement) 1 (Pour le M1 Quantitative Economics)
Recommended prerequisites :
Most of the course is self-contained but the students are expected to be familiar with all the mathematical tools associated to an economics curriculum: Linear algebra, calculus, continuous optimization, probability and statistics, all at an undergraduate level. A significant part of the examples of data manipulation from the course will make use of this mathematical knowledge. However, the course should be accessible even with only a cursory knowledge of most of the listed concepts.
Learning outcomes :
The first objective of the course is to introduce students to data-driven projects, by presenting the first steps of such projects from data collection to data exploration. Acknowledging the strong limitations of integrated software that rely solely (or mostly) on graphical user interfaces, the second major objective of the course is to provide all the programming knowledge and tools needed to implement all those data management steps, relying on the R language.
After having attended the classes, the students will be able to:
- specify a data management chain adapted to a data-driven project;
- identify the potential data value increase at the different steps of the chain;
- implement those steps in R: data cleaning, data storage, data aggregation and other requests, data exploration;
- more generally implement non-obvious data manipulation schemes in R;
- write active documents using quarto;
- use git and github at a basic level.
Assessment :
The final grade will be made of two types of grading: A continuous assessment grade, made mostly of grades obtained to quizzes (approximately 50 % of the grade) and integrating oral participation during the class and regular attendance; A grade obtained on a full data-oriented project from data collection to data exploration (preferably done in groups of 2 students).
Bibliography-recommended reading
R for data science: r4ds.hadley.nz
- Macroeconometrics
Macroeconometrics
Ects : 6
Lecturer :
- MATTEO MOGLIANI
Total hours : 36
Overview :
This course will provide the fundamental tools in macroeconometrics. It starts providing the basic knowledge on the modelling of univariate time series, the concept of stationarity, the main tools to represent a univariate time series. Then, it will show some extensions to this basic framework (time varying parameters, selection of variables…). The course will also introduce to forecasting. We will then present the modelling of multivariate time series with VAR models, explain how structural VAR analysis is the natural set up to depart from a purely statistical description and provide economic interpretation. Finally, different extensions to this set up will be introduced: time-varying parameters, co-integration, expectations ….
Coefficient : 1
Recommended prerequisites :
statistics, general mathematical background
Learning outcomes :
The objective of the course is to provide the student with the solid theoretical and practical knowledge of the methods used to analyse and model time series data. Practical skills will be acquired through the modelling of economic time series with econometric software (practical sessions under Matlab). After having attended the classes, the students will master the main tools of time series’ modelling and be able to run an empirical work by themselves.
Assessment :
Final Exam (50%) + Final Project in pairs (40%) + Participation (10%)
Bibliography-recommended reading
Hamilton, J.D. (1994). Time Series Analysis, Princeton University Press. Johnston, J. and J.E. DiNardo (2007), Econometric Methods, Mac Graw-Hill Econometric series.
Mandatory courses - 21 ECTS
- Game theory
Game theory
Ects : 6
Lecturer :
Total hours : 36
Overview :
Chapter 1: Normal form games: pure and mixed strategy Nash equilibrium; weakly/strictly dominated strategies , iterated elimination of dominated strategies.
Chapter 2: Dynamic games: Backward induction, subgame perfect Nash equilibrium, repeated games.
Chapter 3: Incomplete information (in static games): Bayesian Nash equilibrium; introduction to some applications (auctions, finance...)
Coefficient : 1
Require prerequisites :
The student must be at ease with some basic mathematical notions such as: derivations, first-order conditions...
Learning outcomes :
The objective of the course is to give some fundamental background in interactive decision making and its applications. After having attended the classes, the students will be able to understand the basic tools of game theory and the importance of this field in economics and finance.
Assessment :
A mid-term exam and a final exam
Choose one track
ECONOMICS TRACK
Research Track - AQME Certificate courses (Graduate Program in Economy - 9 ECTS
- Applied Microeconometrics
Applied Microeconometrics
Ects : 6
Lecturer :
Total hours : 30
Overview :
This course focuses on micro-econometrics techniques based on temporal data (cross-sectional and panel) and qualitative dependent variables. The first part will explore possible sources of OLS bias and discuss techniques and estimators to address those biases ( micro-econometrics techniques for temporal data, such as first difference, random effects, fixed effects and difference-in-differences estimators). Non-linear models (Probit, Logit models), as well as selection models (Tobit, Heckman selection models) will be the focus of the second part of the course, as well as the instrumental variable estimator. The main themes are presented under a theoretical perspective, accompanied by empirical applications on Stata.
Coefficient : 1
Require prerequisites :
Statistics and Probability, statistical inference, hypothesis testing, OLS with multiple variables
Learning outcomes :
At the end of the course the students will master the main micro-econometrics techniques for probability models and temporal data and they will be able to critically analyze applied work that employs these types of estimators.
Assessment :
Students will be evaluated in two steps. They will present in pairs a scientific paper among a list provided by the teacher. This will be the same paper to be replicated for the Database and Stata Programming course. The presentation will count for 30% of the final note. The rest of the note will be based on a final written exam scheduled in the exams’ week.
Bibliography-recommended reading
List of scientific papers for students’ presentations will be provided at the beginning of the course. Selected chapters from :
- Wooldridge, J. (2002) "Econometric analysis of cross-section and panel data", MIT Press, Cambridge.
- A. Colin Cameron and Pravin K. Trivedi (2005), "Microeconometrics: Methods and Applications", Cambridge University Press
All slides, datasets, papers and other materials will be available on the MyCourse webpage.
- Microeconometrics : data applications
Microeconometrics : data applications
Ects : 3
Lecturer :
Total hours : 24
Overview :
The course presents the Stata coding language for applying micro-econometrics techniques. In the first part of the course, the main Stata features are explained by focusing on the estimation of econometric models with qualitative variables and selection models. In the second part of the course, students will learn how to analyse temporal and panel data with Stata and how to estimate temporal models, such as random effects, fixed effects and double differences. Moreover, the course will provide students with the appropriate knowledge for reproducing their econometric analyses in a professional format.
Coefficient : 0,5
Recommended prerequisites :
Statistics and Probability, statistical inference, hypothesis testing, OLS with multiple variables
Learning outcomes :
The main objective of this course is to provide students with Stata coding skills for describing and analysing cross-sectional and panel data and for estimating probability and temporal econometric models.
After having attended the classes, the students will be able to describe and analyze phenomena of interest contained in cross-sectional and panel datasets by using Stata. They will be able to conduct econometric analysis concerning probability and temporal models with graphs and tables formatted in a professional manner.
Assessment :
Critical analysis and replication of a research paper’s results in a short dissertation format.
Bibliography-recommended reading
1. Cameron, Adrian Colin, and Pravin K. Trivedi. Microeconometrics using stata. Vol. 2. College Station, TX: Stata press, 2010.
2. Gentzkow and Shapiro (2014) “Code and Data for the Social Sciences: A Practitioner’s Guide.”
Internet resources:
1. Stata video tutorials: https://www.stata.com/links/video-tutorials/
2. UCLA tips: http://www.ats.ucla.edu/stata/
Mandatory courses - 12 ECTS
Electives - 6 ECTS - Choose 2
Open your mind - 3 ECTS
DATA TRACK
Mandatory courses - 21 ECTS
Open your mind - 3 ECTS
Electives - Choose for 6 ECTS
- Applied Microeconometrics
Applied Microeconometrics
Ects : 6
Lecturer :
Total hours : 30
Overview :
This course focuses on micro-econometrics techniques based on temporal data (cross-sectional and panel) and qualitative dependent variables. The first part will explore possible sources of OLS bias and discuss techniques and estimators to address those biases ( micro-econometrics techniques for temporal data, such as first difference, random effects, fixed effects and difference-in-differences estimators). Non-linear models (Probit, Logit models), as well as selection models (Tobit, Heckman selection models) will be the focus of the second part of the course, as well as the instrumental variable estimator. The main themes are presented under a theoretical perspective, accompanied by empirical applications on Stata.
Coefficient : 1
Require prerequisites :
Statistics and Probability, statistical inference, hypothesis testing, OLS with multiple variables
Learning outcomes :
At the end of the course the students will master the main micro-econometrics techniques for probability models and temporal data and they will be able to critically analyze applied work that employs these types of estimators.
Assessment :
Students will be evaluated in two steps. They will present in pairs a scientific paper among a list provided by the teacher. This will be the same paper to be replicated for the Database and Stata Programming course. The presentation will count for 30% of the final note. The rest of the note will be based on a final written exam scheduled in the exams’ week.
Bibliography-recommended reading
List of scientific papers for students’ presentations will be provided at the beginning of the course. Selected chapters from :
- Wooldridge, J. (2002) "Econometric analysis of cross-section and panel data", MIT Press, Cambridge.
- A. Colin Cameron and Pravin K. Trivedi (2005), "Microeconometrics: Methods and Applications", Cambridge University Press
All slides, datasets, papers and other materials will be available on the MyCourse webpage.
Academic Training Year 2025 - 2026 - subject to modification
Teaching Modalities
All courses in the first year of the Master’s in Quantitative Economics are taught in English and in lecture format. In some cases, part of the session may be devoted to correcting exercises and/or data processing. The first semester starts in early September with a 10-day refresher training course in statistical tools and Matlab programming. The first semester then lasts 12 weeks, with foundational courses in economics (Macroeconomics I, Microeconomics I, Game Theory), alongside two courses in data processing and analysis: Macroeconometrics and Data Management and Programming. Students are introduced to Matlab, Dynare and R software. All courses are mandatory, and amount to 30 ECTS credits. Courses and final exams end in December of the academic year.
The second semester expands on the lessons of the first semester. In terms of theory, this involves integrating market failures and frictions into economic analysis (Microeconomics II, Industrial Organization). A topical lecture also raises students’ awareness on how economic research can help addressing a selected set of contemporary issues at the heart of policy and economic debates. In terms of quantitative methods, instruction focuses on techniques for analyzing individual and qualitative data using Stata (Microeconometrics, with Application to Stata). In addition to these required courses, students choose two among five optional courses (Health Economics ; Measurement issues with applications to GDP, poverty and inequality ; International Trade ; Macroeconomics II ; Advanced Industrial Organization). Each student must earn 30 ECTS credits by the semester's end. Courses are spread out over 12 weeks from January to April, and exams are held in early May. Students are then strongly encouraged to pursue an internship, although this will not earn ECTS credits.
After the Master first year, the students can opt for a gap year before pursuing in one of the two Master 2 tracks. Only gap year projects that include a relevant pr ofessional experience (internship of short-term contract) and/or an exchange study program through the QTEM network will be accepted by the Master 2 directors. Applicants should have a strong and reliable project and shall discuss with the targeted Master 2 director during the Master first year to better prepare this gap year in line with the Master 2 training.
Internships and Supervised Projects
Students are not required to do an internship during the first year of their Master's in Quantitative Economics. However, they are strongly encouraged to pursue one after the second semester’s exams, although this will not earn ECTS credits.
Research-driven Programs
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Research is organized around 6 disciplines all centered on the sciences of organizations and decision making.
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