Le programme de la formation
AQME CERTIFICATE (9 ECTS)
- Machine Learning
Machine Learning
Ects : 6
Enseignant responsable :
FABRICE ROSSIVolume horaire : 36
Description du contenu de l'enseignement :
The course gives a thorough presentation of the machine learning field and follows this outline:
- general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
- machine learning as automated program building from examples (running example: decision trees)
- machine learning as optimization: empirical risk minimizationlinks with maximum likelihood estimationsurrogate losses and extended machine learning settingsregularisation and kernel methods (support vector machines)
- reliable estimation of performances: over fittingsplit samplesresampling (leave-one-out, cross-validation and bootstrap)ROC curve, AUC and other advanced measures
- combining models: ensemble techniquesbagging and random forestsboosting
- unsupervised learning: clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)outlier and anomaly detection
Coefficient : 2 6 (M2 Economie Internationale et Développement) 6 (M2 Diagnostic économique international)
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 :
INES MOURELONVolume horaire : 12
- Python for data science
Python for data science
Ects : 3
Enseignant responsable :
MOHAMED KHALIL EL MAHRSIVolume 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.
Parcours Theory
Cours obligatoire (theory S3)
- Master Thesis project
Master Thesis project
Ects : 3
Enseignant responsable :
LISE PATUREAUDescription du contenu de l'enseignement :
The purpose is to help the student to build his Master thesis subject. The student is expected to work on his own on his Master thesis subject, with the help of her Master supervisor if needed. There is no session in class associated with this course. The student is expected to write a summary of her Master thesis project specifying: the supervisor, the general topic; the summary of the research question and what is the (expected) original contribution ; the data to be used, the method of implementation shall also be clarified.
Coefficient : Validation
Compétences à acquérir :
Acquire an overview of a research topic, identify the key relevant research papers and the methodology used to address the question
Mode de contrôle des connaissances :
There is no grade associated to this course. Validation will be granted if the student provide the summary of the Master thesis project by the due time.
- Conférence cycle : International Organizations & Job Market Information
Conférence cycle : International Organizations & Job Market Information
Enseignant responsable :
FLORENCE ARESTOFF
NAJAT EL MEKKAOUIVolume horaire : 21
Description du contenu de l'enseignement :
The aim of these seminars series is to present the main International organizations, how they operate, their objectives, their missions and the major issues they face at international level.
Professionals from these international institutions will provide information regarding internships and employment opportunities within their organizations, along with the criteria they consider when evaluating internship or job applications.
The International organizations taking part at this seminars series are: OECD, IMF, World Bank, European Commission, HCR, EIB, ECB, Club de Paris, IFC, MIGA, WTO.
Compétences à acquérir :
These seminars help students understand how to join an international organisation, do an internship there and find a job there.
- Behavioral economics and bounded rationality
Behavioral economics and bounded rationality
Ects : 3
Enseignant responsable :
BERTRAND VILLENEUVEVolume 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, in particular 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 :
- MCQs all along the classes (30%).
- Final written exam (70%).
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. Some of them are heavily used for the lectures.
- Ex-ante policy evaluation : Methods and applications
Ex-ante policy evaluation : Methods and applications
Ects : 3
Enseignant responsable :
DANIEL HERRERAVolume horaire : 21
Coefficient : 2
- Advanced Game Theory
Advanced Game Theory
Ects : 3
Enseignant responsable :
SIDARTHA GORDONVolume 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.
Options I : choose from the two blocks
Block 1
- Methods for public policy evaluation
Methods for public policy evaluation
Ects : 6
Enseignant responsable :
ERIC BONSANGVolume horaire : 27
Description du contenu de l'enseignement :
This course explores different topics in applied microeconometrics at advanced level for public policy evaluation. It focuses on causal inference and how econometrics can help identify causality. It discusses the advantages and limitations of particular types of approaches/tools that are used in econometrics. It covers the following topics: Causal inference and identification, Randomized experiment, Regression and causality, Instrumental variables approach and Regression discontinuity designs. The course will review the theory underlying those different techniques and will discuss the recent studies that have applied these methods to make causal inference.
Coefficient : 2
Pré-requis recommandés :
M1 Course: Microeconometrics
Compétences à acquérir :
The objective of the course is to provide students the econometric methods aiming at identifying causal relationships. These methods are widely applied in economics to assess the effects of policy interventions and other treatment on interest. After attending the classes, the students will be able to have a deep understanding and a critical view on studies aiming at identifying causal effects and to apply those methods for their own research.
Mode de contrôle des connaissances :
Written exam (70%) + Short empirical paper (20%) + Active participation in class (10%)
Bibliographie-lectures recommandées
Mostly Harmless Econometrics, Joshua Angrist and Jörn-Steffen Pischke
Econometric Analysis of Cross-section and Panel Data, Jeffrey Wooldridge
Microeconometrics. Methods and Applications, A. Colin Cameron and Pravin K. Trivedi
Block 2
- Advanced Macroeconometrics
Advanced Macroeconometrics
Ects : 6
Enseignant responsable :
FABIEN TRIPIERVolume horaire : 27
Coefficient : 2
- Bayesian techniques in macroeconomics
Bayesian techniques in macroeconomics
Ects : 3
Enseignant responsable :
GAUTHIER VERMANDELVolume horaire : 12
Coefficient : 1
- Finance in continuous time (mandatory course, unless validated previously)
Finance in continuous time (mandatory course, unless validated previously)
Ects : 6
Enseignant responsable :
RENE AIDVolume horaire : 30
Description du contenu de l'enseignement :
Asset pricing, contingent claim, stochastic process, brownian motion, Itô's formula, optimal stopping time. This course is an introduction to "Derivative pricing and stochastic calculus II". It introduces the standard concepts and tools allowing to understand arbitrage theory in continuous-time. The requirements from probability theory are made as basic as possible to make the lectures accessible to studends without a strong background in applied mathematics.
Coefficient : 1 (Master Finance) 3ECTS - Coefficient 1 (M2 Quantitative Economics)
Compétences à acquérir :
In the end of this course, the students must be comfortable with:
i) Basic concepts of contingent claims,
ii) the binomial model;
iii) stochastic integrals and Itôs calculus;
iv) the Black and Scholes model,
v) Merton's optimal porfolio problem.
Bibliographie-lectures recommandées
Steven Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, 2005.
Steven Shreve, Stochastic Calculus for Finance II: Continuous-Time Models , 2005.
Options II - choose one
- Asset pricing theory
Asset pricing theory
Ects : 6
Enseignant responsable :
JEROME DUGASTVolume horaire : 30
Description du contenu de l'enseignement :
In this course, we will discuss a wide range of topics ranging from optimal portfolio, the CAPM, factor models, consumption-based asset pricing, and arbitrage pricing, to more special ones including asymmetric information, and limits to arbitrage.
- Optimal Portfolio Theory and the CAPM
- Factor Models
- Decision Making under Uncertainty
- Consumption-based Asset Pricing
- Arbitrage Pricing
- Dynamic Asset Pricing
- Asymmetric Information and Asset Prices
- Limits to Arbitrage
Coefficient : 1 (M1 Finance) 3ECTS - Coefficient 2 (M2 Quantitative Economics)
Compétences à acquérir :
Master the theoretical concepts of asset pricing
Mode de contrôle des connaissances :
Evaluation: assignment 20%, final exam 80%
- Inequality and redistribution
Inequality and redistribution
Ects : 3
Enseignant responsable :
LAURA KHOURYVolume horaire : 18
Description du contenu de l'enseignement :
In most developed countries, inequality has been rising in recent decades, becoming a key political issue at the center of the public debate. This course aims at understanding the historical evolution of between- and within-country inequality from the late 19th century until today, and what are the key drivers explaining this evolution. How to adequately measure inequality? How does globalization impact global inequality? What is the effect of technological change on labor income inequality? What is the role of public policies in mitigating these effects? We will review economic theories and use up-to-date empirical techniques to address these questions. Through the presentations of recent research papers, students will also get acquainted with the multiple dimensions of inequality (e.g. gender inequality, racial inequality, inequality in education outcomes, etc.).
Coefficient : 2
Pré-requis obligatoire :
Statistics (Basic level)
Microeconometrics (M1 mandatory course)
Compétences à acquérir :
At the end of the course, students should be able to:
- Describe the evolution of income inequality in developed and developing countries since the 19th century
- Identify and describe the drivers of the change in labor and capital inequality
- Understand and use models to rationalize the change in labor and capital inequality
- Understand and design policy tools that can mitigate inequality through redistribution
Mode de contrôle des connaissances :
Assessment will be based on a presentation (30%), a final written exam (65%) and participation in class (5%). The presentation will consist in presenting in class a research paper addressing the question of inequality. The final exam will be a mix of short questions about concepts seen in class and question where the student will be asked to develop his own analysis using the concepts seen in class.
Bibliographie-lectures recommandées
A specific reading list is provided at the start of each session.
- Environement and sustainability
Environement and sustainability
Ects : 3
Enseignant responsable :
ANNA CRETIVolume horaire : 21
Description du contenu de l'enseignement :
Global warming and the related environmental and social issues raise serious concerns for the welfare of our current and future generations. Such changes require to develop new approaches and solutions to address these key issues so that they can become and remain sustainable. The course Environment and Sustainability will introduce students to key theories and models related to the environment, sustainability, societal issues, and the United Nations' Sustainable Development Goals.
1. Introduction: challenges for sustainability toward the net-zero economy
2. Sustainability: definition and examples
3.Sustainability: theoretical challenges
4.Climate Change: definition and examples
5.Climate Change: theoretical challenges
6.Climate Change policies
7.The energy transition
Coefficient : 2 3 (M2 Economie Internationale et Développement)
Pré-requis recommandés :
Advanced Micro and Macro Economics
Compétences à acquérir :
Students will be able to critically evaluate the complex drivers and consequences of global environmental problems for different societal groups, applying academic concepts and theories. They will develop in-depth knowledge in specialist areas of environment and sustainability and gain critical thinking skills. Finally, attendees will be able to assess the effectiveness, equity and trade-offs of different sustainability goals and policies.
Mode de contrôle des connaissances :
Final Exam (written dissertation)
Bibliographie-lectures recommandées
Dasgupta, Sir Partha. "The Economics of Biodiversity The Dasgupta Review Abridged Version." (2021).
Richard S. J. Tol, Climate Economics: Economic Analysis of Climate, Climate Change and Climate Policy Edward Elgar Publishing, 2019 - 234 pages
Selected Videos from rtol.github.io/ClimateEconomics/video/
Parcours Social and Public Policies
Mandatory courses (Social and Public Policies)
- Master Thesis project
Master Thesis project
Ects : 3
Enseignant responsable :
LISE PATUREAUDescription du contenu de l'enseignement :
The purpose is to help the student to build his Master thesis subject. The student is expected to work on his own on his Master thesis subject, with the help of her Master supervisor if needed. There is no session in class associated with this course. The student is expected to write a summary of her Master thesis project specifying: the supervisor, the general topic; the summary of the research question and what is the (expected) original contribution ; the data to be used, the method of implementation shall also be clarified.
Coefficient : Validation
Compétences à acquérir :
Acquire an overview of a research topic, identify the key relevant research papers and the methodology used to address the question
Mode de contrôle des connaissances :
There is no grade associated to this course. Validation will be granted if the student provide the summary of the Master thesis project by the due time.
- Conférence cycle : International Organizations & Job Market Information
Conférence cycle : International Organizations & Job Market Information
Enseignant responsable :
FLORENCE ARESTOFF
NAJAT EL MEKKAOUIVolume horaire : 21
Description du contenu de l'enseignement :
The aim of these seminars series is to present the main International organizations, how they operate, their objectives, their missions and the major issues they face at international level.
Professionals from these international institutions will provide information regarding internships and employment opportunities within their organizations, along with the criteria they consider when evaluating internship or job applications.
The International organizations taking part at this seminars series are: OECD, IMF, World Bank, European Commission, HCR, EIB, ECB, Club de Paris, IFC, MIGA, WTO.
Compétences à acquérir :
These seminars help students understand how to join an international organisation, do an internship there and find a job there.
- Methods for public policy evaluation
Methods for public policy evaluation
Ects : 6
Enseignant responsable :
ERIC BONSANGVolume horaire : 27
Description du contenu de l'enseignement :
This course explores different topics in applied microeconometrics at advanced level for public policy evaluation. It focuses on causal inference and how econometrics can help identify causality. It discusses the advantages and limitations of particular types of approaches/tools that are used in econometrics. It covers the following topics: Causal inference and identification, Randomized experiment, Regression and causality, Instrumental variables approach and Regression discontinuity designs. The course will review the theory underlying those different techniques and will discuss the recent studies that have applied these methods to make causal inference.
Coefficient : 2
Pré-requis recommandés :
M1 Course: Microeconometrics
Compétences à acquérir :
The objective of the course is to provide students the econometric methods aiming at identifying causal relationships. These methods are widely applied in economics to assess the effects of policy interventions and other treatment on interest. After attending the classes, the students will be able to have a deep understanding and a critical view on studies aiming at identifying causal effects and to apply those methods for their own research.
Mode de contrôle des connaissances :
Written exam (70%) + Short empirical paper (20%) + Active participation in class (10%)
Bibliographie-lectures recommandées
Mostly Harmless Econometrics, Joshua Angrist and Jörn-Steffen Pischke
Econometric Analysis of Cross-section and Panel Data, Jeffrey Wooldridge
Microeconometrics. Methods and Applications, A. Colin Cameron and Pravin K. Trivedi
- Labor, education and public policies
Labor, education and public policies
Ects : 3
Enseignant responsable :
GABRIELLE FACK
LIONEL WILNERVolume horaire : 24
Description du contenu de l'enseignement :
This course will present an overview of topics in Labour and Education. The first part of the course will cover 4 topics related to wage determination and unemployment insurance, which are at the frontier of current research in labour economics. More specifically, we will consider the returns to education, the fundamentals of wage determination, the forms and consequences of labour market discrimination and unemployment insurance. The course will cover both the theoretical and empirical aspects of all topics. It will also systematically discuss the relevant policy implications. The second part of the course will review the reasons for government intervention in Education, and will then cover three main types of interventions: demand side policies (financial and information interventions), supply side policies (school resources) and policies aimed at reducing inequalities (affirmative action).
Provisional schedule
1. Labour I: Returns to education
2. Labour II: Wage determination and minimum wage
3. Labour III: Labour market discrimination
4. Labour IV: Unemployment insurance
5. Education I: demand side policies (financial and information interventions)
6. Education II: supply side policies (school resources)
7. Education III : policies aimed at reducing inequalities (affirmative action)
Coefficient : 2
Pré-requis obligatoire :
Graduate Microeconomics
Graduate Econometrics
Compétences à acquérir :
The first objective of the course is to equip the students with the tools that will allow them to understand the contemporary labour market and the relevant public policies. With this aim, it will first provide students with advanced knowledge of the determinants of wages, both from a theoretical and an empirical perspective. At the end of the course, the students will be able to identify the mechanisms underlying wage setting within firms and will have a good understanding of the main quantitative methods used by labour economists. They will also be able to contribute to the design of public policies related to labour market discrimination, unemployment insurance, etc.
The second objective of the course is provide students with a critical analysis of government intervention in education. It will present an overview of the main types of education policies, together with in-depth empirical analysis of the impact of specific policies. At the end of the course, the students will be able to identify the market failures and equity issues that concern education, and the type of policies that may be considered to solve them. They will also have a good understanding of the main quantitative methods used by economists to evaluate the impact of educational policies and contribute to the social debate on education.
This class will be useful to students who want to do a PhD dissertation in the field of applied labour economics and education economics as well as to students who plan to work in institutions that produce studies and policy recommendations regarding education and the labour market, such as the OECD, Ministries of Labour, the ILO, etc.
Mode de contrôle des connaissances :
Written and oral assessment
During the course, students will be asked to present an article chosen in the reading list of the course. This presentation will be graded. A final exam will take place during the exam week. The final grade will be computed as a weighted average of the oral presentation (30%) and written exam (65%) grades, as well as a grade to account for participation (5%).
Bibliographie-lectures recommandées
Tito Boeri and Jan Van Ours, The Economics of Imperfect Labour Markets, 2nd edition, Princeton University Press, 2013.
Articles listed on the reading list provided at the start of the course
- Inequality and redistribution
Inequality and redistribution
Ects : 3
Enseignant responsable :
LAURA KHOURYVolume horaire : 18
Description du contenu de l'enseignement :
In most developed countries, inequality has been rising in recent decades, becoming a key political issue at the center of the public debate. This course aims at understanding the historical evolution of between- and within-country inequality from the late 19th century until today, and what are the key drivers explaining this evolution. How to adequately measure inequality? How does globalization impact global inequality? What is the effect of technological change on labor income inequality? What is the role of public policies in mitigating these effects? We will review economic theories and use up-to-date empirical techniques to address these questions. Through the presentations of recent research papers, students will also get acquainted with the multiple dimensions of inequality (e.g. gender inequality, racial inequality, inequality in education outcomes, etc.).
Coefficient : 2
Pré-requis obligatoire :
Statistics (Basic level)
Microeconometrics (M1 mandatory course)
Compétences à acquérir :
At the end of the course, students should be able to:
- Describe the evolution of income inequality in developed and developing countries since the 19th century
- Identify and describe the drivers of the change in labor and capital inequality
- Understand and use models to rationalize the change in labor and capital inequality
- Understand and design policy tools that can mitigate inequality through redistribution
Mode de contrôle des connaissances :
Assessment will be based on a presentation (30%), a final written exam (65%) and participation in class (5%). The presentation will consist in presenting in class a research paper addressing the question of inequality. The final exam will be a mix of short questions about concepts seen in class and question where the student will be asked to develop his own analysis using the concepts seen in class.
Bibliographie-lectures recommandées
A specific reading list is provided at the start of each session.
- Health, welfare and health behavior
Health, welfare and health behavior
Ects : 3
Enseignant responsable :
PETER EIBICHVolume horaire : 21
Coefficient : 2
Options - Choose one
- Behavioral economics and bounded rationality
Behavioral economics and bounded rationality
Ects : 3
Enseignant responsable :
BERTRAND VILLENEUVEVolume 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, in particular 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 :
- MCQs all along the classes (30%).
- Final written exam (70%).
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. Some of them are heavily used for the lectures.
- Ex-ante policy evaluation : Methods and applications
Ex-ante policy evaluation : Methods and applications
Ects : 3
Enseignant responsable :
DANIEL HERRERAVolume horaire : 21
Coefficient : 2
- Environement and sustainability
Environement and sustainability
Ects : 3
Enseignant responsable :
ANNA CRETIVolume horaire : 21
Description du contenu de l'enseignement :
Global warming and the related environmental and social issues raise serious concerns for the welfare of our current and future generations. Such changes require to develop new approaches and solutions to address these key issues so that they can become and remain sustainable. The course Environment and Sustainability will introduce students to key theories and models related to the environment, sustainability, societal issues, and the United Nations' Sustainable Development Goals.
1. Introduction: challenges for sustainability toward the net-zero economy
2. Sustainability: definition and examples
3.Sustainability: theoretical challenges
4.Climate Change: definition and examples
5.Climate Change: theoretical challenges
6.Climate Change policies
7.The energy transition
Coefficient : 2 3 (M2 Economie Internationale et Développement)
Pré-requis recommandés :
Advanced Micro and Macro Economics
Compétences à acquérir :
Students will be able to critically evaluate the complex drivers and consequences of global environmental problems for different societal groups, applying academic concepts and theories. They will develop in-depth knowledge in specialist areas of environment and sustainability and gain critical thinking skills. Finally, attendees will be able to assess the effectiveness, equity and trade-offs of different sustainability goals and policies.
Mode de contrôle des connaissances :
Final Exam (written dissertation)
Bibliographie-lectures recommandées
Dasgupta, Sir Partha. "The Economics of Biodiversity The Dasgupta Review Abridged Version." (2021).
Richard S. J. Tol, Climate Economics: Economic Analysis of Climate, Climate Change and Climate Policy Edward Elgar Publishing, 2019 - 234 pages
Selected Videos from rtol.github.io/ClimateEconomics/video/
Parcours Macro & FInance
Mandatory courses (Macro & FInance)
- Master Thesis project
Master Thesis project
Ects : 3
Enseignant responsable :
LISE PATUREAUDescription du contenu de l'enseignement :
The purpose is to help the student to build his Master thesis subject. The student is expected to work on his own on his Master thesis subject, with the help of her Master supervisor if needed. There is no session in class associated with this course. The student is expected to write a summary of her Master thesis project specifying: the supervisor, the general topic; the summary of the research question and what is the (expected) original contribution ; the data to be used, the method of implementation shall also be clarified.
Coefficient : Validation
Compétences à acquérir :
Acquire an overview of a research topic, identify the key relevant research papers and the methodology used to address the question
Mode de contrôle des connaissances :
There is no grade associated to this course. Validation will be granted if the student provide the summary of the Master thesis project by the due time.
- Conférence cycle : International Organizations & Job Market Information
Conférence cycle : International Organizations & Job Market Information
Enseignant responsable :
FLORENCE ARESTOFF
NAJAT EL MEKKAOUIVolume horaire : 21
Description du contenu de l'enseignement :
The aim of these seminars series is to present the main International organizations, how they operate, their objectives, their missions and the major issues they face at international level.
Professionals from these international institutions will provide information regarding internships and employment opportunities within their organizations, along with the criteria they consider when evaluating internship or job applications.
The International organizations taking part at this seminars series are: OECD, IMF, World Bank, European Commission, HCR, EIB, ECB, Club de Paris, IFC, MIGA, WTO.
Compétences à acquérir :
These seminars help students understand how to join an international organisation, do an internship there and find a job there.
- Advanced Macroeconometrics
Advanced Macroeconometrics
Ects : 6
Enseignant responsable :
FABIEN TRIPIERVolume horaire : 27
Coefficient : 2
- International Trade & International Macroeconomics
International Trade & International Macroeconomics
Ects : 3
Enseignant responsable :
GIANLUCA OREFICEVolume horaire : 24
Description du contenu de l'enseignement :
The course covers the recent advances in international trade and macroeconomics with an emphasis on the role of firm heterogeneity. Starting from recent models of international trade with heterogeneous firms (Melitz 2003; Chaney 2008) and its effects on the labor market, the course will rely on the theoretical modelling of the New Open Economy Macroeconomy framework (Obstfeld & Rogoff, 1995), which embeds explicit microfoundations in a dynamic general equilibrium perspective. The first part of the course will provide students with the essential tools to study the optimal international strategy of firms with different levels of productivity. The second part of the course studies the recent advances in international macroeconomics that incorporate these elements from the international trade literature, by modeling the role of the extensive margin of trade à la Melitz (2003) in an international macroeconomic setting.
Coefficient : 2
Pré-requis recommandés :
Good knowledge of basic models of international trade and macroeconomics
Compétences à acquérir :
The objective of the course is to introduce some key topics of interest in the field of international trade and international macroeconomics and to provide students with the modelling framework to address them. A specific focus will be made on the role of firm heterogeneity in shaping international trade flows as well as macroeconomic fluctuations in an international set-up. The students will be trained to read leading research articles on these issues. After attending the classes, the students will have a sharp understanding of the optimal international strategy of firms, and how such trade microfoundations shed new light on long-standing or novel questions in international macroeconomics. They will also master the cutting-edge research at the frontier between international macroeconomics and international trade, and how to think about economic policy in this global framework.
Mode de contrôle des connaissances :
Final exam: 60%
Home assignment: 40%
Bibliographie-lectures recommandées
There is no textbook for this course. We will base entirely on published academic papers, based on the (yet non-definitive) list of papers.
Common core paper - compulsory reading
· Melitz, M. (2003) “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity”, Econometrica 71: 1695-1725
Part I: International trade
· Brainard, S.L. (1997) “An Empirical Assessment of the Proximity- Concentration Trade-off Between Multinational Sales and Trade,” American Economic Review, 87(4), pages 520-544 (suggested reading)
· Melitz, M., Helpman, H. and S. Yeaple (2004) “Export Versus FDI with Heterogeneous Firms”, American Economic Review 94: 300-316 (compulsory reading).
· Pavcnik (2002) “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants”, The Review of Economic Studies 69, January 2002, pp. 245-76 (suggested reading).
· Trefler D. (2004) “The Long and Short of the Canada-U.S. Free Trade Agreement”, American Economic Review 94: 870-895 (compulsory reading).
· Helpman H. and Itskhoki (2010) “Labour Market Rigidities, Trade and Unemployment”, Review of Economic Studies, 77(3): 1100-1137 (compulsory reading).
· Kovak, B. (2013) “Regional Effects of Trade Reform: What is the Correct Measure of Liberalization”, American Economic Review, 103(5): 1960-1976 (compulsory reading).
· Autor D., Dorn D., and G. Hanson (2013) “The China Syndrome: Local Labor Market Effects of Import Competition in the United States”, American Economic Review, 2013, 103(6), 2121–2168 (compulsory reading).
· Kovak, B and R. Dix-Carneir o (2017) “Trade Liberalization and Regional Dynamics”, American Economic Review, 107(10): 1908-2946 (suggested reading).
Part II: International Macroeconomics
Trade, trade integration and international macroeconomics
· Backus, David K.; Kehoe, Patrick J.; Kydland, Finn E. (1995), "International Business Cycles: Theory and Evidence", in Cooley, Tom (ed.), Frontiers of Business Cycle Research, Princeton University Press
· Ghironi, Fabio, and Marc Melitz. 2005. “International Trade and Macroeconomic Dynamics with Heterogeneous Firms.” Quarterly Journal of Economics 120: 865-915
Firm heterogeneity, firm dynamics and international fluctuations
· Barratieri, Alessandro, Cacciatore, Matteo, Ghironi, Fabio; « Protectionism and the business cycle », Journal of International Economics, vol. 129, 2021, p. 1-21
· Monetary policy, firm heterogeneity, and product variety, Hamano, Masashige and Zanetti, Francesco, European Economic Review, Vol. 104, 2022
· Cacciatore M., Ghironi F., “Trade, unemployment, and monetary policy”, Journal of International Economics, 2021
Trade, granularity and business cycles
· The Micro Origins of International Business-Cycle Comovement, Julian di Giovanni, Andrei A. Levchenko and Isabelle Mejean, American Economic Review, Vol 108, 2018
· Large Firms and International Business Cycle Comovement, 2017, American Economic Review P&P, 107(5):598-602, J. di Giovanni, A. Levchenko and I. Méjean
· Volatility in the small and in the large: The lack of diversification in international trade, Francis Kramarz, Julien Martin and Isabelle Méjean, Journal of International Economics, Volume 122, January 2020
The macro consequences of economic uncertainty in a globalized world
· The economic effects of trade policy uncertai nty, Dario Caldara, Matteo Iacoviello, Patrick Molligo, Andrea Prestipino and Andrea Raffo, Journal of Monetary Economics, Volume 109, January 2020
· Brexit and the macroeconomic impact of trade policy uncertainty, Steinberg, J., 2019. Journal of International Economics, 117, 175-195.
- Key challenges for Advanced macroeconomics
Key challenges for Advanced macroeconomics
Ects : 3
Enseignant responsable :
FABIEN TRIPIERVolume horaire : 24
Coefficient : 2
Options I - choose one
- Bayesian techniques in macroeconomics
Bayesian techniques in macroeconomics
Ects : 3
Enseignant responsable :
GAUTHIER VERMANDELVolume horaire : 12
Coefficient : 1
- Finance in continuous time (mandatory course, unless validated previously)
Finance in continuous time (mandatory course, unless validated previously)
Ects : 6
Enseignant responsable :
RENE AIDVolume horaire : 30
Description du contenu de l'enseignement :
Asset pricing, contingent claim, stochastic process, brownian motion, Itô's formula, optimal stopping time. This course is an introduction to "Derivative pricing and stochastic calculus II". It introduces the standard concepts and tools allowing to understand arbitrage theory in continuous-time. The requirements from probability theory are made as basic as possible to make the lectures accessible to studends without a strong background in applied mathematics.
Coefficient : 1 (Master Finance) 3ECTS - Coefficient 1 (M2 Quantitative Economics)
Compétences à acquérir :
In the end of this course, the students must be comfortable with:
i) Basic concepts of contingent claims,
ii) the binomial model;
iii) stochastic integrals and Itôs calculus;
iv) the Black and Scholes model,
v) Merton's optimal porfolio problem.
Bibliographie-lectures recommandées
Steven Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, 2005.
Steven Shreve, Stochastic Calculus for Finance II: Continuous-Time Models , 2005.
Options II - choose two courses among these
- Asset pricing theory
Asset pricing theory
Ects : 6
Enseignant responsable :
JEROME DUGASTVolume horaire : 30
Description du contenu de l'enseignement :
In this course, we will discuss a wide range of topics ranging from optimal portfolio, the CAPM, factor models, consumption-based asset pricing, and arbitrage pricing, to more special ones including asymmetric information, and limits to arbitrage.
- Optimal Portfolio Theory and the CAPM
- Factor Models
- Decision Making under Uncertainty
- Consumption-based Asset Pricing
- Arbitrage Pricing
- Dynamic Asset Pricing
- Asymmetric Information and Asset Prices
- Limits to Arbitrage
Coefficient : 1 (M1 Finance) 3ECTS - Coefficient 2 (M2 Quantitative Economics)
Compétences à acquérir :
Master the theoretical concepts of asset pricing
Mode de contrôle des connaissances :
Evaluation: assignment 20%, final exam 80%
- Behavioral economics and bounded rationality
Behavioral economics and bounded rationality
Ects : 3
Enseignant responsable :
BERTRAND VILLENEUVEVolume 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, in particular 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 :
- MCQs all along the classes (30%).
- Final written exam (70%).
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. Some of them are heavily used for the lectures.
- Quantitative International Economics
Quantitative International Economics
Ects : 3
Enseignant responsable :
FARID TOUBALVolume horaire : 21
Description du contenu de l'enseignement :
This lecture covers advanced topics in international economics with a special emphasis on quantitative techniques employed in international trade. This course is divided into two main components: the first part introduces important concepts and provides the theoretical foundations of the structural gravity equation. The second part deals with partial and general equilibrium trade policy analysis with structural gravity
Coefficient : 2 (M2 Quantitative Economics) 3 (M2 Economie Internationale et Développement)
Pré-requis recommandés :
Solid knowledge in Microeconomics, Econometrics and International Trade
Pré-requis obligatoire :
Microeconomics, Macroeconomics, Econometrics, International Trade
Compétences à acquérir :
- Enhance their understanding of economic methods and data sources for trade policy analysis.
- Applying international trade models and provides recommendations on how to obtain reliable partial and general equilibrium estimates for the effects of trade policy.
Mode de contrôle des connaissances :
Home works
Bibliographie-lectures recommandées
- Head K. and T. Mayer, 2014. "https://09d9bfd0-a-62cb3a1a-s-sites.googlegroups.com/site/hiegravity/gravityHB_pub.pdf?attachauth=ANoY7co_ceVRmljbk5jVk4bZ31ErA03LfZ0rteg5hKLne2Z1LVXcthU-4O5ODiP1BKvcEOicRclcEL1hQbtbQpwQx8OVMFVJqvDhuC2cMknjn0Pc2L-SvqEqSHefwY2QzAU4czdzvtlu7hTRVFulcSJcZTTuG5h6TAaVopKxiSADnidb8cFJxDzg-yiBryeqEa92AxmibuuUe_CmbRp6d_kBSsF5RQ2yaQ%3D%3D&attredirects=0Gravity Equations: Workhorse,Toolkit, and Cookbook", Handbook of International Economics, 4th ed, 4:131-195.
- sites.google.com/site/hiegravity/Gravity Cookbook website
- Costinot, A., and A. Rodríguez-Clare, 2014. "https://economics.mit.edu/files/9960Trade Theory with Numbers: Quantifying the Consequences of Globalization", Handbook of International Economics, 4th ed, 4:131-195.
- Yotov, Y. V., Piermartini, R., Monteiro, J. A., & Larch, M. (2016). yotoyotov.com/book.htmlAn advanced guide to trade policy analysis: The structural gravity model. Geneva: World Trade Organization.
common to the three
Track Recherche PG ECO
- Machine Learning
Machine Learning
Ects : 6
Enseignant responsable :
FABRICE ROSSIVolume horaire : 36
Description du contenu de l'enseignement :
The course gives a thorough presentation of the machine learning field and follows this outline:
- general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
- machine learning as automated program building from examples (running example: decision trees)
- machine learning as optimization: empirical risk minimizationlinks with maximum likelihood estimationsurrogate losses and extended machine learning settingsregularisation and kernel methods (support vector machines)
- reliable estimation of performances: over fittingsplit samplesresampling (leave-one-out, cross-validation and bootstrap)ROC curve, AUC and other advanced measures
- combining models: ensemble techniquesbagging and random forestsboosting
- unsupervised learning: clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)outlier and anomaly detection
Coefficient : 2 6 (M2 Economie Internationale et Développement) 6 (M2 Diagnostic économique international)
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 :
INES MOURELONVolume horaire : 12
- Python for data science
Python for data science
Ects : 3
Enseignant responsable :
MOHAMED KHALIL EL MAHRSIVolume 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.
common to the three
Data science course (3ECTS) optional course : one to choose for 3 ECTS
- NLP for economic decisions
NLP for economic decisions
Ects : 3
Volume horaire : 24
Coefficient : 2
- Machine Learning for Applied Economic Analysis
Machine Learning for Applied Economic Analysis
Ects : 3
Enseignant responsable :
MATHILDE GODARDVolume horaire : 24
Description du contenu de l'enseignement :
Economic science has evolved over several decades toward greater emphasis on empirical work. Ever increasing mass of available data (’big data’) in the past decade is likely to have a further and profound effect on economic research (Einav and Levin, 2014). Beyond economic research, governments and the industry are also increasingly seeking to use ’big data’ to solve a variety of problems, usually making use of the toolbox from machine learning (ML).
The question we ask in this course is the following : What do we (not) learn from big data and ML as economists? Is ML merely applying standard techniques to novel and large datasets? If ML is a fundamentally new empirical tool, how does it fit with what we know? In particular, how does it fit with our tools for causal inference problems? As empirical economists, how can we use big data and ML? We’ll discuss in detail how ML is useful to collect new data, for prediction in policy, and to provide new tools for estimation and inference.
Coefficient : 2
Pré-requis recommandés :
Python (beginner/intermediate), Machine Learning, Microeconometrics.
Compétences à acquérir :
Course objectives:
1. Present a way of thinking about ML that gives it its own place in the econometric toolbox.
2 Develop an intuition of the problems to which it can be applied in economics, and its limitations.
3 Data challenge in health policy.
Mode de contrôle des connaissances :
Grading:
1. In-class pairwise presentation of an academic paper (20% of overall grade).
2. Data challenge project : written report + in-class presentation (80% of overall grade).
Bibliographie-lectures recommandées
- Mullainathan, Sendhil and Jann Spiess (2017). “Machine learning: An applied econometric approach”. In: Journal of Economic Perspective 31.2, pp. 87-106.
- Kleinberg, Jon et al. (2015). “Prediction policy problems”. American Economic Review 105.5, pp. 491-495.
- Athey, S. (2017): “Beyond prediction: Using big data for policy problems”, Science 355, 483–485.
- Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. and S. Mullainathan (2018): “Human Decisions and Machine Predictions”, The Quarterly Journal of Economics, Volume 133, Issue 1, Pages 237–293.
- Susan Athey, Guido W. Imbens. 2019. Machine Learning Methods That Economists Should Know About. Annual Review of Economics 11:1, 685-725.
- Athey, Susan, and Guido Imbens. 2016. “Recursive Partitioning for Heterogeneous Causal Effects”. PNAS 113(27): 7353–60.
- Belloni, A., V. Chernozhukov, S. Mullainathan and J. Spiess and C. Hansen.(2014): “High-Dimensional Methods and Inference on Structural and Treatment Effects” Journal of Economic Perspectives, Volume 28, Number 2 – Spring 2014, Pages 29–50
Parcours Theory
mandatory courses (Theory)
- Master Thesis Defense
Master Thesis Defense
Ects : 15
Coefficient : 9
- Mater Thesis support seminar
Mater Thesis support seminar
- PhD Proposal / Internship
PhD Proposal / Internship
Ects : 6
Specialization field : Theory - choose two courses among these
- Banking economics
Banking economics
Ects : 3
Enseignant responsable :
SYLVAIN CARREVolume horaire : 18
Description du contenu de l'enseignement :
This course provides students with an in-depth introduction to banking economics. Students will be taken through the main challenges in Banking (financial stability, fire sales phenomena, regulation and moral hazard…) by studying some of the key papers in the literature and learning their main modelling techniques. Both long-lasting and more recent issues will be addressed, with a particular focus on the set of problems and debates that arose during the 2007-2009 Great Financial Crisis. Once equipped with the key concepts of banking theory, students will be introduced to the main policy instruments available to regulators for dampening the above-mentioned problems. We will study the nature and role of the Basel III agreements, as well as discuss their possible costs and benefits. We will also study the impact of other types of regulatory activities, notably stress tests and disclosures, and explain how policies aimed at market beliefs are complementary to those aimed at banks’ balance sheet and operational decisions.
Coefficient : 2
Pré-requis recommandés :
Basic microeconomic knowledge, basic notions of calculus and optimization, basic notions of game theory.
Compétences à acquérir :
Students will get acquainted to the modern modelling tools for Banking economics. They will see how these tools allow to shed light on both traditional banking issues (bank runs, moral hazard) and more recent ones (shadow banking, regulatory arbitrage). We will then study how regulation can help in addressing these issues and aim at understanding the raison-d’être of several policy instruments, ranging from the Basel III rules to stress test results disclosure strategies.
Mode de contrôle des connaissances :
Presentation 50% Written exam 50%
- Computational social choice
Computational social choice
Ects : 3
Enseignant responsable :
JEROME LANGVolume horaire : 24
Coefficient : 1
- Incremental learning, game theory and applications
Incremental learning, game theory and applications
Ects : 3
Enseignant responsable :
MOHAMMED RIDA LARAKIVolume horaire : 24
Coefficient : 1
Parcours Social and Public Policies
Mandatory courses (Social and Public Policies)
- PhD Proposal / Internship
PhD Proposal / Internship
Ects : 6
- Master Thesis Defense
Master Thesis Defense
Ects : 15
Coefficient : 9
- Mater Thesis support seminar
Mater Thesis support seminar
- Advanced Health economics
Advanced Health economics
Ects : 3
Enseignant responsable :
ELSA PERDRIXVolume horaire : 21
Coefficient : 2
- Policies in developing countries
Policies in developing countries
Ects : 3
Enseignant responsable :
OLIVIA BERTELLIVolume horaire : 18
Coefficient : 2
Parcours Macro & FInance
Mandatory courses (Macro & FInance)
- PhD Proposal / Internship
PhD Proposal / Internship
Ects : 6
- Master Thesis Defense
Master Thesis Defense
Ects : 15
Coefficient : 9
- Mater Thesis support seminar
Mater Thesis support seminar
- Banking economics
Banking economics
Ects : 3
Enseignant responsable :
SYLVAIN CARREVolume horaire : 18
Description du contenu de l'enseignement :
This course provides students with an in-depth introduction to banking economics. Students will be taken through the main challenges in Banking (financial stability, fire sales phenomena, regulation and moral hazard…) by studying some of the key papers in the literature and learning their main modelling techniques. Both long-lasting and more recent issues will be addressed, with a particular focus on the set of problems and debates that arose during the 2007-2009 Great Financial Crisis. Once equipped with the key concepts of banking theory, students will be introduced to the main policy instruments available to regulators for dampening the above-mentioned problems. We will study the nature and role of the Basel III agreements, as well as discuss their possible costs and benefits. We will also study the impact of other types of regulatory activities, notably stress tests and disclosures, and explain how policies aimed at market beliefs are complementary to those aimed at banks’ balance sheet and operational decisions.
Coefficient : 2
Pré-requis recommandés :
Basic microeconomic knowledge, basic notions of calculus and optimization, basic notions of game theory.
Compétences à acquérir :
Students will get acquainted to the modern modelling tools for Banking economics. They will see how these tools allow to shed light on both traditional banking issues (bank runs, moral hazard) and more recent ones (shadow banking, regulatory arbitrage). We will then study how regulation can help in addressing these issues and aim at understanding the raison-d’être of several policy instruments, ranging from the Basel III rules to stress test results disclosure strategies.
Mode de contrôle des connaissances :
Presentation 50% Written exam 50%
- Advanced environmental macroeconomics
Advanced environmental macroeconomics
Ects : 3
Enseignant responsable :
GAUTHIER VERMANDELVolume horaire : 15
Coefficient : 2
Formation année universitaire 2024 - 2025 - sous réserve de modification
Modalités pédagogiques
All courses in the Quantitative Economic Analysis track 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. As the program emphasizes research training, students will frequently read research articles, with the possibility of presenting some of them in front of the class. The first semester goes from September to December. The second semester starts in January and ends in early May. On completing each semester, students will receive 30 ECTS credits.
There are two mandatory courses during the first semester:Information EconomicsandIntroduction to Machine Learning. The selection of other courses depends on the student's chosen specialization, out of 3 options:Social and Public Policies,Theory, andMacroeconomics and Finance. During the first semester, courses should be selected within two distinct blocks: Quantitative Methods and Specialization Courses. During the second semester, students take three courses and a research seminar corresponding to their specialization. During the second semester, students must also write a dissertation, to be submitted and defended in June. There will be a poster session sometime in March for students to present their progress to the class and the instructors.
Stages et projets tutorés
Each student in the Quantitative Economic Analysis track individually writes a research thesis, which counts for 18 ECTS credits (out of 30 credits in the 2nd semester). This must be submitted and defended at the start of June. There will be a poster session sometime in March for students to present their progress to the class and the instructors.
The end-of-year internship is not required. However, students are strongly encouraged to pursue one after the second semester’s exams, although this will not be rewarded with ECTS credits.
Des programmes nourris par la recherche
Les formations sont construites au contact des programmes de recherche de niveau international de Dauphine, qui leur assure exigence et innovation.
La recherche est organisée autour de 6 disciplines toutes centrées sur les sciences des organisations et de la décision.
En savoir plus sur la recherche à Dauphine