Quantitative Economic Analysis - 296 - 2nd year of master's degree

AQME CERTIFICATE (9 ECTS)

• Machine Learning

  Machine Learning

  Ects : 6

  Enseignant responsable :
  FABRICE ROSSI
  

  Volume horaire : 36

  Description du contenu de l'enseignement :

  The course gives a thorough presentation of the machine learning field and follows this outline:

  1. general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
  2. machine learning as automated program building from examples (running example: decision trees)
  3. machine learning as optimization: empirical risk minimizationlinks with maximum likelihood estimationsurrogate losses and extended machine learning settingsregularisation and kernel methods (support vector machines)
  4. reliable estimation of performances: over fittingsplit samplesresampling (leave-one-out, cross-validation and bootstrap)ROC curve, AUC and other advanced measures
  5. combining models: ensemble techniquesbagging and random forestsboosting
  6. unsupervised learning: clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)outlier and anomaly detection

  Coefficient : 2

  Pré-requis obligatoire :

  • intermediate level in either Python or R. Students are expected to be able to perform standard data management tasks in Python or R, including, but not limited to: loading a data set from a CSV filerecoding and cleaning the data set implementing a simple data exploration strategy based on pivot table and on graphical representation
  • intermediate level in statistics and probability. Students are expected to be familiar with: descriptive statisticsconditional probabilities and conditional expectationscore results from statistics: bias and variance concepts, strong law of large numbers, central limit theorem, etc.

  Compétences à acquérir :

  After attending the course the students will

  • have a good understanding of the algorithmic and statistical foundations of the main machine learning techniques
  • be able to select machine learning techniques adapted to a particular task (exploratory analysis with clustering methods, predictive analysis, etc.)
  • be able to design a model selection procedure adapted to a particular task
  • report the results of a machine learning project with valid estimation of the performances of their model

  Mode de contrôle des connaissances :

  • quizzes and tests during the course
  • machine learning project
• Introduction to Matlab programming

  Introduction to Matlab programming

  Enseignant responsable :
  CEDRIC CROFILS
  

  Volume horaire : 12
• Python for data science

  Python for data science

  Ects : 3

  Enseignant responsable :
  MOHAMED KHALIL EL MAHRSI
  

  Volume horaire : 18

  Description du contenu de l'enseignement :

  The course is organised as follows.

   

  1 - Introduction to Python Programming

   

  This first part introduces the fundamentals of Python programming. It covers topics such as working with basic built-in types (numbers, strings, booleans, ...), control flow statements, writing reusable code (functions), handling errors and exception that can occur during the execution of Python code, advanced data structures (lists, sets, dictionaries, ...), ...

   

  2 - Scientific Computing With NumPy

   

  This part focuses on using NumPy, a scientific computing package that provides a wide assortment of useful and highly-optimized routines for working with multi-dimensional arrays (matrices, tensors, ...), linear algebra, statistics and random simulation, and much more.

   

  3 - Processing Tabular Data With pandas

   

  The third part of the course is dedicated to pandas, a fundamental Python package when it comes to data science and data analysis. pandas provides functionalities for efficient manipulation of data frames, i.e., tabular data (stored in csv files, Excel sheets, ...). With the help of pandas, you can easily conduct tasks such as data cleaning (filling missing data, replacing outliers, ...), reshaping, merging, ...

   

  4 - Visualizing Data With Matplotlib and seaborn

   

  The last part of the course is a quick introduction to data visualization functionalities in Python using the Matplotlib and seaborn packages. Data visualization is a very powerful tool for making sens of large volumes of data, identifying patterns, and extracting useful insights that can help understand and solve real-world business cases.

  Coefficient : 1

  Pré-requis recommandés :

  The course does not assume any prior knowledge in programming in general and Python in particular. However, familiarity with another programming language can be useful in understanding the discussed concepts and topics.

  Pré-requis obligatoire :

  You are expected to be familiar with mathematical tools associated to an economics curriculum (linear algebra, calculus, probability, and statistics) at an undergraduate level

  Compétences à acquérir :

  By the end of this course, you will be able to

  • Write and understand entry-level to intermediate-level code in the Python programming language
  • Use NumPy for scientific computing and efficient manipulation of multi-dimensional arrays and matrices
  • Use pandas to load, manipulate, and analyze tabular data
  • Use Matplotlib and seaborn to visualize data

  Mode de contrôle des connaissances :

  You will be evaluated based on a team project (conducted in pairs) in which you will apply the knowledge and skills you acquired during the course. The project takes the form of an exploratory data analysis in which you will work on a tabular data set in order to extract valuable insights that can help solve a business problem. The expected deliverables of the project are:

  • A 5–10 pages report;
  • The source code (Jupyter notebooks or Python scripts) of your work, either in a Github repository or as a zip file.

  You are expected to present your main findings during a 10-minutes presentation, which will be followed by approximatively 5 minutes of questions.

MANDATORY COURSES

• Master Thesis project

  Master Thesis project

  Ects : 3

  Enseignant responsable :
  LISE PATUREAU
  

  Coefficient : Validation
• Conference cycles: International Organizations & Job Market Informations

  Conference cycles: International Organizations & Job Market Informations

  Coefficient : Validation

OPTIONAL COURSE (6 ECTS) MANDATORY WITHIN THE SPECIALIZATION FIELD

Specialization field : Theory

• Methods for public policy evaluation

  Methods for public policy evaluation

  Ects : 6

  Enseignant responsable :
  ERIC BONSANG
  

  Volume horaire : 27

  Coefficient : 2
• Advanced Macroeconometrics

  Advanced Macroeconometrics

  Ects : 6

  Enseignant responsable :
  FABIEN TRIPIER
  

  Volume horaire : 27

  Coefficient : 2

Specialization field: Social and Public Policies

• Methods for public policy evaluation

  Methods for public policy evaluation

  Ects : 6

  Enseignant responsable :
  ERIC BONSANG
  

  Volume horaire : 27

  Coefficient : 2

Specialization field: Macro & Finance

• Advanced Macroeconometrics

  Advanced Macroeconometrics

  Ects : 6

  Enseignant responsable :
  FABIEN TRIPIER
  

  Volume horaire : 27

  Coefficient : 2

SPECIALIZATION COURSES (12 ECTS) MANDATORY WITHIN THE FIELD

Specialization field: Theory - Mandatory courses

• Behavioral economics and bounded rationality

  Behavioral economics and bounded rationality

  Ects : 3

  Enseignant responsable :
  BERTRAND VILLENEUVE
  

  Volume horaire : 21

  Description du contenu de l'enseignement :

  The objective of the course is to present the most important themes in behavioral economics.

  • Reference-dependent utility, with and without risk
  • Probabilistic judgement and the treatment of information
  • Time preferences
  • Attention and inattention
  • Social preferences

  The course itself will focus on models and their empirical validity. By choice, the course will not be principally about experimental protocols - yet protocols are explained occasionally - but rather on main ideas, results, and debates. The diverse applications will be treated all along.

  Coefficient : 2

  Pré-requis obligatoire :

  Expected utility. Basic game theory. Basic probability theory, comprised Bayesian calculus.

  Compétences à acquérir :

  The topic has reached a certain degree of maturity and it is part of an aspiring economist culture. After attending the classes, the students will be able to read the cutting-edge research on the topic. Given the variety of ways by which standard (non behavioral) models can be tweaked, the course is not intended to promote a particular view, but to help would-be modelers to better motivate their choices.

  Mode de contrôle des connaissances :

  • Participation.
  • Short presentation of a paper in class.
  • Final written exam.

  Bibliographie-lectures recommandées

  Highly recommended for the fascinating and lively excursion across almost all topics: Daniel Kahneman's 2011 book, Thinking Fast and Slow.

  The main reference is the Handbook of Behavioral Economics, Elsevier, 2018 and 2019. All chapters are dense and fascinating. Some of them are heavily used for the lectures.

• Empirical Industrial Organization

  Empirical Industrial Organization

  Ects : 3

  Enseignant responsable :
  DANIEL HERRERA
  

  Volume horaire : 21

  Description du contenu de l'enseignement :

  In this course we will cover mainstream empirical industrial organization methods. The main goal is to provide a set of tools necessary to undertake empirical analyses typically performed in Empirical Industrial Organization. Most methods that will be reviewed in this course are not limited to empirical IO, but can be used in a variety of different fields such as health, finance, and environmental economics. The course will consider reduced-form estimation papers, seeking to provide insights from data to understand how markets work. To generate policy-relevant counterfactuals, the course will also deal with structural estimation of supply and demand models. Reduced and structural econometrics methods requires the use of programs such as Matlab. Practical tutorials will ensure the implementation of the materials provided in the course.

  Coefficient : 2

  Pré-requis recommandés :

  Basic programming skills on Matlab or similar software.

  Pré-requis obligatoire :

  Theoretical Industrial Organization and advanced econometrics.

  Compétences à acquérir :

  After having attended the classes, the students will have an overview of seminal and recent papers in empirical IO; understand core empirical methods and the data requirements for each method to be implemented. They will also have a working knowledge on Matlab.

  Bibliographie-lectures recommandées

  Entry models and market structure: estimation of fixed costs

  • [***] Berry, S., & Reiss, P. (2007). Empirical models of entry and market structure. Handbook of industrial organization, 3, 1845-1886.
  • [***] T. Bresnahan and P. Reiss, “Econometric Models of Discrete Games,” Journal of Econometrics, 1991a.
  • [***] T. Bresnahan, and P. Reiss, “Entry and Competition in Concentrated Markets,” Journal of Political Economy, 1991b.
  • [***] S. Berry, “Estimation of a Model of Entry in the Airline Industry,” Econometrica, 1992.
  • S. Berry and J. Waldfogel, “Free Entry and Social Inefficiency in Radio Broadcasting,” RAND Journal of Economics, 1999.
  • O. Toivanen, and M. Waterson, “Market Structure and Entry: Where’s the Beef?” RAND Journal of Economics, 2005.
  • K. Seim, “An Empirical Model of Firm Entry with Endogenous Product–Type Choices,” RAND Journal of Economics, 2006.
  • Verboven, F., & Schaumans, C. Entry and competition in concentrated markets with product differentiation. Review of Economics and Statistics, 97(1), 195-209, 2015
  • P. Jia, “What Happens When Wal-Mart Comes to Town: An Empirical Analysis of the Discount Retail Industry,” EMA, November 2008, 1263-316.
  • M. Mazzeo, “Product Choice and Oligopoly Market Structure,” RJE, Summer 2002, 221-42.
  • Schaumans, C. and F. Verboven (2008), "Entry and Regulation: Evidence from Health care Professions," RAND Journal of Economics, 39(4), pp. 949–972.

  Estimation of demand and marginal costs

  • [***] Steven T. Berry, “Estimating Discrete-Choice Models of Product Differentiation,” Rand Journal of Economics, 25, 242-262, 1994.
  • [***] Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica: Journal of the Econometric Society, 841-890.
  • [***] A. Nevo, “Identification of the Oligopoly Solution Concept in a Differentiated– Prod ucts Industry,” Economics Letters, 1998.
  • [***] Nevo, A. (2000). Mergers with differentiated products: The case of the ready-to-eat cereal industry. The RAND Journal of Economics, 395-421.
  • [***] A. Nevo, “Empirical Models of Consumer Behavior,” Annual Review of Economics, 2011.
  • Berry, S., & Pakes, A. (2007). The pure characteristics demand model. International Economic Review, 48(4), 1193-1225
  • Goldberg, P. K. (1995). Product differentiation and oligopoly in international markets: The case of the US automobile industry. Econometrica: Journal of the Econometric Society, 891-951
  • A. Goolsbee and A. Petrin, “The Consumer Gains from Direct Broadcast Satellites and the Competition with Cable TV,” Econometrica, 2004.
  • C. Knittel and K. Metaxoglou, “Estimation of Random–Coefficient Demand Models: Two Empiricists’ Perspective,” The Review of Economics and Statistics, 2014.
  • A. Nevo, “A Practitioner’s Guide to Estimation of Random–Coefficients Logit Models of Demand,” Journal of Economics & Management Strategy, 2000b.
  • A. Nevo, “Measuring Market Power in the Ready–To–Eat Cereal Industry,” Econometrica, 2001.
• Advanced Game Theory

  Advanced Game Theory

  Ects : 3

  Enseignant responsable :
  FRANCOISE FORGES
  

  Volume horaire : 18

  Description du contenu de l'enseignement :

  The course is divided into two parts. The first part is devoted to so-called “noncooperative games” and concentrates on multistage games with incomplete information played by Bayesian players. The agents’ rationality is analyzed through various solution concepts, capturing backward and/or forward induction. These solution concepts are applied to strategic information transmission and communication. In the second part, we will first focus on a particular class of games of strategic information transmission, the class of unidimensional cheap talk sender receiver-games, and then introduce recent models on the choice of an information structure by a designer (or principal) for an agent or a set of agents who interact strategically in an asymmetric information setting.  

  Coefficient : 2

  Compétences à acquérir :

  After having attended the classes, the students will be able to read recent academic papers applying game theory to various area of economics and to make use of game theory in their future research work.

Specialization field: Theory - Optional courses (Choose one)

• Asset pricing Theory

  Asset pricing Theory

  Ects : 3

  Enseignant responsable :
  JEROME DUGAST
  

  Volume horaire : 27

  Coefficient : 2
• Inequality and redistribution

  Inequality and redistribution

  Ects : 3

  Volume horaire : 18

  Coefficient : 2
• Environement and sustainability

  Environement and sustainability

  Ects : 3

  Enseignant responsable :
  ANNA CRETI
  

  Volume horaire : 21

  Coefficient : 2

Specialization field: Social and Public Policies - Mandatory courses

• Labor & Education economics

  Labor & Education economics

  Ects : 3

  Enseignant responsable :
  EVE CAROLI
  

  Volume horaire : 24

  Description du contenu de l'enseignement :

  This course will cover a number of topics at the frontier of current research in labour economics:

  • Returns to education
  • Compensating wage differentials
  • Wage inequality
  • Skill-biased technical change
  • Discrimination on the labour market
  • Monopsony on the labour market

  The objective of the course is to provide students with advanced knowledge of a series of topics that are key to public policy in the field of labour. The course will cover both the theoretical and empirical aspects of all topics. It will also systematically discuss the relevant policy implications.

  Coefficient : 2

  Pré-requis recommandés :

  Labour Economics (undergraduate)

  Pré-requis obligatoire :

  Microeconomics (graduate level)

  Compétences à acquérir :

  After attending the course, the students will have acquired the tools that are necessary to analyse public policies in the field of labour. They will also have an excellent mastering of the nature of these policies and be equipped to make policy recommendations in the field of labour.

  Mode de contrôle des connaissances :

  Written exam

• Inequality and redistribution

  Inequality and redistribution

  Ects : 3

  Volume horaire : 18

  Coefficient : 2
• Health, welfare and health behavior

  Health, welfare and health behavior

  Ects : 3

  Coefficient : 2

Specialization field: Social and Public Policies - Optional courses (Choose one)

• Behavioral economics and bounded rationality

  Behavioral economics and bounded rationality

  Ects : 3

  Enseignant responsable :
  BERTRAND VILLENEUVE
  

  Volume horaire : 21

  Description du contenu de l'enseignement :

  The objective of the course is to present the most important themes in behavioral economics.

  • Reference-dependent utility, with and without risk
  • Probabilistic judgement and the treatment of information
  • Time preferences
  • Attention and inattention
  • Social preferences

  The course itself will focus on models and their empirical validity. By choice, the course will not be principally about experimental protocols - yet protocols are explained occasionally - but rather on main ideas, results, and debates. The diverse applications will be treated all along.

  Coefficient : 2

  Pré-requis obligatoire :

  Expected utility. Basic game theory. Basic probability theory, comprised Bayesian calculus.

  Compétences à acquérir :

  The topic has reached a certain degree of maturity and it is part of an aspiring economist culture. After attending the classes, the students will be able to read the cutting-edge research on the topic. Given the variety of ways by which standard (non behavioral) models can be tweaked, the course is not intended to promote a particular view, but to help would-be modelers to better motivate their choices.

  Mode de contrôle des connaissances :

  • Participation.
  • Short presentation of a paper in class.
  • Final written exam.

  Bibliographie-lectures recommandées

  Highly recommended for the fascinating and lively excursion across almost all topics: Daniel Kahneman's 2011 book, Thinking Fast and Slow.

  The main reference is the Handbook of Behavioral Economics, Elsevier, 2018 and 2019. All chapters are dense and fascinating. Some of them are heavily used for the lectures.

• Empirical Industrial Organization

  Empirical Industrial Organization

  Ects : 3

  Enseignant responsable :
  DANIEL HERRERA
  

  Volume horaire : 21

  Description du contenu de l'enseignement :

  In this course we will cover mainstream empirical industrial organization methods. The main goal is to provide a set of tools necessary to undertake empirical analyses typically performed in Empirical Industrial Organization. Most methods that will be reviewed in this course are not limited to empirical IO, but can be used in a variety of different fields such as health, finance, and environmental economics. The course will consider reduced-form estimation papers, seeking to provide insights from data to understand how markets work. To generate policy-relevant counterfactuals, the course will also deal with structural estimation of supply and demand models. Reduced and structural econometrics methods requires the use of programs such as Matlab. Practical tutorials will ensure the implementation of the materials provided in the course.

  Coefficient : 2

  Pré-requis recommandés :

  Basic programming skills on Matlab or similar software.

  Pré-requis obligatoire :

  Theoretical Industrial Organization and advanced econometrics.

  Compétences à acquérir :

  After having attended the classes, the students will have an overview of seminal and recent papers in empirical IO; understand core empirical methods and the data requirements for each method to be implemented. They will also have a working knowledge on Matlab.

  Bibliographie-lectures recommandées

  Entry models and market structure: estimation of fixed costs

  • [***] Berry, S., & Reiss, P. (2007). Empirical models of entry and market structure. Handbook of industrial organization, 3, 1845-1886.
  • [***] T. Bresnahan and P. Reiss, “Econometric Models of Discrete Games,” Journal of Econometrics, 1991a.
  • [***] T. Bresnahan, and P. Reiss, “Entry and Competition in Concentrated Markets,” Journal of Political Economy, 1991b.
  • [***] S. Berry, “Estimation of a Model of Entry in the Airline Industry,” Econometrica, 1992.
  • S. Berry and J. Waldfogel, “Free Entry and Social Inefficiency in Radio Broadcasting,” RAND Journal of Economics, 1999.
  • O. Toivanen, and M. Waterson, “Market Structure and Entry: Where’s the Beef?” RAND Journal of Economics, 2005.
  • K. Seim, “An Empirical Model of Firm Entry with Endogenous Product–Type Choices,” RAND Journal of Economics, 2006.
  • Verboven, F., & Schaumans, C. Entry and competition in concentrated markets with product differentiation. Review of Economics and Statistics, 97(1), 195-209, 2015
  • P. Jia, “What Happens When Wal-Mart Comes to Town: An Empirical Analysis of the Discount Retail Industry,” EMA, November 2008, 1263-316.
  • M. Mazzeo, “Product Choice and Oligopoly Market Structure,” RJE, Summer 2002, 221-42.
  • Schaumans, C. and F. Verboven (2008), "Entry and Regulation: Evidence from Health care Professions," RAND Journal of Economics, 39(4), pp. 949–972.

  Estimation of demand and marginal costs

  • [***] Steven T. Berry, “Estimating Discrete-Choice Models of Product Differentiation,” Rand Journal of Economics, 25, 242-262, 1994.
  • [***] Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica: Journal of the Econometric Society, 841-890.
  • [***] A. Nevo, “Identification of the Oligopoly Solution Concept in a Differentiated– Prod ucts Industry,” Economics Letters, 1998.
  • [***] Nevo, A. (2000). Mergers with differentiated products: The case of the ready-to-eat cereal industry. The RAND Journal of Economics, 395-421.
  • [***] A. Nevo, “Empirical Models of Consumer Behavior,” Annual Review of Economics, 2011.
  • Berry, S., & Pakes, A. (2007). The pure characteristics demand model. International Economic Review, 48(4), 1193-1225
  • Goldberg, P. K. (1995). Product differentiation and oligopoly in international markets: The case of the US automobile industry. Econometrica: Journal of the Econometric Society, 891-951
  • A. Goolsbee and A. Petrin, “The Consumer Gains from Direct Broadcast Satellites and the Competition with Cable TV,” Econometrica, 2004.
  • C. Knittel and K. Metaxoglou, “Estimation of Random–Coefficient Demand Models: Two Empiricists’ Perspective,” The Review of Economics and Statistics, 2014.
  • A. Nevo, “A Practitioner’s Guide to Estimation of Random–Coefficients Logit Models of Demand,” Journal of Economics & Management Strategy, 2000b.
  • A. Nevo, “Measuring Market Power in the Ready–To–Eat Cereal Industry,” Econometrica, 2001.
• Environement and sustainability

  Environement and sustainability

  Ects : 3

  Enseignant responsable :
  ANNA CRETI
  

  Volume horaire : 21

  Coefficient : 2

Specialization field: Macro & Finance - Mandatory courses

• International Trade & International Macroeconomics

  International Trade & International Macroeconomics

  Ects : 3

  Enseignant responsable :
  GIANLUCA OREFICE
  

  Volume horaire : 24

  Coefficient : 2
• Business Cycles and Stabilization policies

  Business Cycles and Stabilization policies

  Ects : 3

  Enseignant responsable :
  ANNE EPAULARD
  

  Volume horaire : 24

  Coefficient : 2

Specialization field: Macro & Finance - Optional courses (Choose 2)

• Asset pricing Theory

  Asset pricing Theory

  Ects : 3

  Enseignant responsable :
  JEROME DUGAST
  

  Volume horaire : 27

  Coefficient : 2
• Behavioral economics and bounded rationality

  Behavioral economics and bounded rationality

  Ects : 3

  Enseignant responsable :
  BERTRAND VILLENEUVE
  

  Volume horaire : 21

  Description du contenu de l'enseignement :

  The objective of the course is to present the most important themes in behavioral economics.

  • Reference-dependent utility, with and without risk
  • Probabilistic judgement and the treatment of information
  • Time preferences
  • Attention and inattention
  • Social preferences

  The course itself will focus on models and their empirical validity. By choice, the course will not be principally about experimental protocols - yet protocols are explained occasionally - but rather on main ideas, results, and debates. The diverse applications will be treated all along.

  Coefficient : 2

  Pré-requis obligatoire :

  Expected utility. Basic game theory. Basic probability theory, comprised Bayesian calculus.

  Compétences à acquérir :

  The topic has reached a certain degree of maturity and it is part of an aspiring economist culture. After attending the classes, the students will be able to read the cutting-edge research on the topic. Given the variety of ways by which standard (non behavioral) models can be tweaked, the course is not intended to promote a particular view, but to help would-be modelers to better motivate their choices.

  Mode de contrôle des connaissances :

  • Participation.
  • Short presentation of a paper in class.
  • Final written exam.

  Bibliographie-lectures recommandées

  Highly recommended for the fascinating and lively excursion across almost all topics: Daniel Kahneman's 2011 book, Thinking Fast and Slow.

  The main reference is the Handbook of Behavioral Economics, Elsevier, 2018 and 2019. All chapters are dense and fascinating. Some of them are heavily used for the lectures.

• Quantitative International Economics

  Quantitative International Economics

  Ects : 3

  Enseignant responsable :
  FARID TOUBAL
  

  Volume horaire : 21

  Coefficient : 2

MANDATORY COURSES

• PhD Proposal / Internship

  PhD Proposal / Internship

  Ects : 6

  Coefficient : Validation

Master Thesis

• Master Thesis Defense

  Master Thesis Defense

  Ects : 18

  Volume horaire : 11

  Coefficient : Validation
• Master Thesis support seminar

  Master Thesis support seminar

  Volume horaire : 27

  Coefficient : Validation

SPECIALIZATION COURSES (6 ECTS) MANDATORY WITHIN THE SPECIALIZATION FIELD

Specialization field: Theory

• Banking economics

  Banking economics

  Ects : 3

  Enseignant responsable :
  SYLVAIN CARRE
  

  Volume horaire : 18

  Coefficient : 2
• Individual and collective decisions

  Individual and collective decisions

  Ects : 3

  Enseignant responsable :
  JEAN-PHILIPPE LEFORT
  

  Volume horaire : 15

  Coefficient : 2

Specialization field: Social and Public Policies

• Advanced Health economics

  Advanced Health economics

  Ects : 3

  Enseignant responsable :
  BRIGITTE DORMONT
  

  Volume horaire : 21

  Coefficient : 2
• Policies in developing countries

  Policies in developing countries

  Ects : 3

  Enseignant responsable :
  OLIVIA BERTELLI
  

  Volume horaire : 18

  Coefficient : 2

Specialization field: Macro & Finance

• Banking economics

  Banking economics

  Ects : 3

  Enseignant responsable :
  SYLVAIN CARRE
  

  Volume horaire : 18

  Coefficient : 2
• Advanced environmental macroeconomics

  Advanced environmental macroeconomics

  Ects : 3

  Enseignant responsable :
  GAUTHIER VERMANDEL
  

  Volume horaire : 15

  Coefficient : 2