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

AQME CERTIFICATE (9 ECTS)

• Machine Learning

  Machine Learning

  Ects : 6

  Lecturer :
  FABRICE ROSSI
  

  Total hours : 36

  Overview :

  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 6 (M2 Economie Internationale et Développement) 6 (M2 Diagnostic économique international)

  Require prerequisites :

  • 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.

  Learning outcomes :

  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

  Assessment :

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

  Introduction to Matlab programming

  Lecturer :
  INES MOURELON
  

  Total hours : 12
• Python for data science

  Python for data science

  Ects : 3

  Lecturer :
  MOHAMED KHALIL EL MAHRSI
  

  Total hours : 18

  Overview :

  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

  Recommended prerequisites :

  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.

  Require prerequisites :

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

  Learning outcomes :

  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

  Assessment :

  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

  Lecturer :
  LISE PATUREAU
  

  Overview :

  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

  Learning outcomes :

  Acquire an overview of a research topic, identify the key relevant research papers and the methodology used to address the question

  Assessment :

  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

  Lecturer :
  FLORENCE ARESTOFF
  NAJAT EL MEKKAOUI
  

  Total hours : 21

  Overview :

  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.

  Learning outcomes :

  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

  Lecturer :
  BERTRAND VILLENEUVE
  

  Total hours : 21

  Overview :

  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

  Require prerequisites :

  Expected utility. Basic game theory. Basic probability theory, in particular Bayesian calculus.

  Learning outcomes :

  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.

  Assessment :

  • MCQs all along the classes (30%).
  • Final written exam (70%).

  Bibliography-recommended reading

  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

  Lecturer :
  DANIEL HERRERA
  

  Total hours : 21

  Coefficient : 2
• Advanced Game Theory

  Advanced Game Theory

  Ects : 3

  Lecturer :
  SIDARTHA GORDON
  

  Total hours : 18

  Overview :

  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

  Learning outcomes :

  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

  Lecturer :
  ERIC BONSANG
  

  Total hours : 27

  Overview :

  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

  Recommended prerequisites :

  M1 Course: Microeconometrics

  Learning outcomes :

  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.

  Assessment :

  Written exam (70%) + Short empirical paper (20%) + Active participation in class (10%)

  Bibliography-recommended reading

  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

  Lecturer :
  FABIEN TRIPIER
  

  Total hours : 27

  Coefficient : 2
• Bayesian techniques in macroeconomics

  Bayesian techniques in macroeconomics

  Ects : 3

  Lecturer :
  GAUTHIER VERMANDEL
  

  Total hours : 12

  Coefficient : 1
• Finance in continuous time (mandatory course, unless validated previously)

  Finance in continuous time (mandatory course, unless validated previously)

  Ects : 6

  Lecturer :
  RENE AID
  

  Total hours : 30

  Overview :

  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)

  Learning outcomes :

  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.

  Bibliography-recommended reading

  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

  Lecturer :
  JEROME DUGAST
  

  Total hours : 30

  Overview :

  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.

  1. Optimal Portfolio Theory and the CAPM
  2. Factor Models
  3. Decision Making under Uncertainty
  4. Consumption-based Asset Pricing
  5. Arbitrage Pricing
  6. Dynamic Asset Pricing
  7. Asymmetric Information and Asset Prices
  8. Limits to Arbitrage

  Coefficient : 1 (M1 Finance) 3ECTS - Coefficient 2 (M2 Quantitative Economics)

  Learning outcomes :

  Master the theoretical concepts of asset pricing

  Assessment :

  Evaluation: assignment 20%, final exam 80%

• Inequality and redistribution

  Inequality and redistribution

  Ects : 3

  Lecturer :
  LAURA KHOURY
  

  Total hours : 18

  Overview :

  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

  Require prerequisites :

  Statistics (Basic level)

  Microeconometrics (M1 mandatory course)

  Learning outcomes :

  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

  Assessment :

  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.

  Bibliography-recommended reading

  A specific reading list is provided at the start of each session.

• Environement and sustainability

  Environement and sustainability

  Ects : 3

  Lecturer :
  ANNA CRETI
  

  Total hours : 21

  Overview :

  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)

  Recommended prerequisites :

  Advanced Micro and Macro Economics

  Learning outcomes :

  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.

  Assessment :

  Final Exam (written dissertation)

  Bibliography-recommended reading

  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

  Lecturer :
  LISE PATUREAU
  

  Overview :

  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

  Learning outcomes :

  Acquire an overview of a research topic, identify the key relevant research papers and the methodology used to address the question

  Assessment :

  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

  Lecturer :
  FLORENCE ARESTOFF
  NAJAT EL MEKKAOUI
  

  Total hours : 21

  Overview :

  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.

  Learning outcomes :

  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

  Lecturer :
  ERIC BONSANG
  

  Total hours : 27

  Overview :

  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

  Recommended prerequisites :

  M1 Course: Microeconometrics

  Learning outcomes :

  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.

  Assessment :

  Written exam (70%) + Short empirical paper (20%) + Active participation in class (10%)

  Bibliography-recommended reading

  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

  Lecturer :
  GABRIELLE FACK
  LIONEL WILNER
  

  Total hours : 24

  Overview :

  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

  Require prerequisites :

  Graduate Microeconomics

  Graduate Econometrics

  Learning outcomes :

  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.

  Assessment :

  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%).

  Bibliography-recommended reading

  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

  Lecturer :
  LAURA KHOURY
  

  Total hours : 18

  Overview :

  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

  Require prerequisites :

  Statistics (Basic level)

  Microeconometrics (M1 mandatory course)

  Learning outcomes :

  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

  Assessment :

  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.

  Bibliography-recommended reading

  A specific reading list is provided at the start of each session.

• Health, welfare and health behavior

  Health, welfare and health behavior

  Ects : 3

  Lecturer :
  PETER EIBICH
  

  Total hours : 21

  Coefficient : 2

Options - Choose one

• Behavioral economics and bounded rationality

  Behavioral economics and bounded rationality

  Ects : 3

  Lecturer :
  BERTRAND VILLENEUVE
  

  Total hours : 21

  Overview :

  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

  Require prerequisites :

  Expected utility. Basic game theory. Basic probability theory, in particular Bayesian calculus.

  Learning outcomes :

  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.

  Assessment :

  • MCQs all along the classes (30%).
  • Final written exam (70%).

  Bibliography-recommended reading

  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

  Lecturer :
  DANIEL HERRERA
  

  Total hours : 21

  Coefficient : 2
• Environement and sustainability

  Environement and sustainability

  Ects : 3

  Lecturer :
  ANNA CRETI
  

  Total hours : 21

  Overview :

  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)

  Recommended prerequisites :

  Advanced Micro and Macro Economics

  Learning outcomes :

  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.

  Assessment :

  Final Exam (written dissertation)

  Bibliography-recommended reading

  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

  Lecturer :
  LISE PATUREAU
  

  Overview :

  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

  Learning outcomes :

  Acquire an overview of a research topic, identify the key relevant research papers and the methodology used to address the question

  Assessment :

  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

  Lecturer :
  FLORENCE ARESTOFF
  NAJAT EL MEKKAOUI
  

  Total hours : 21

  Overview :

  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.

  Learning outcomes :

  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

  Lecturer :
  FABIEN TRIPIER
  

  Total hours : 27

  Coefficient : 2
• International Trade & International Macroeconomics

  International Trade & International Macroeconomics

  Ects : 3

  Lecturer :
  GIANLUCA OREFICE
  

  Total hours : 24

  Overview :

  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

  Recommended prerequisites :

  Good knowledge of basic models of international trade and macroeconomics

  Learning outcomes :

  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.

  Assessment :

  Final exam: 60%

  Home assignment: 40%

  Bibliography-recommended reading

  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

  Lecturer :
  FABIEN TRIPIER
  

  Total hours : 24

  Coefficient : 2

Options I - choose one

• Bayesian techniques in macroeconomics

  Bayesian techniques in macroeconomics

  Ects : 3

  Lecturer :
  GAUTHIER VERMANDEL
  

  Total hours : 12

  Coefficient : 1
• Finance in continuous time (mandatory course, unless validated previously)

  Finance in continuous time (mandatory course, unless validated previously)

  Ects : 6

  Lecturer :
  RENE AID
  

  Total hours : 30

  Overview :

  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)

  Learning outcomes :

  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.

  Bibliography-recommended reading

  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

  Lecturer :
  JEROME DUGAST
  

  Total hours : 30

  Overview :

  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.

  1. Optimal Portfolio Theory and the CAPM
  2. Factor Models
  3. Decision Making under Uncertainty
  4. Consumption-based Asset Pricing
  5. Arbitrage Pricing
  6. Dynamic Asset Pricing
  7. Asymmetric Information and Asset Prices
  8. Limits to Arbitrage

  Coefficient : 1 (M1 Finance) 3ECTS - Coefficient 2 (M2 Quantitative Economics)

  Learning outcomes :

  Master the theoretical concepts of asset pricing

  Assessment :

  Evaluation: assignment 20%, final exam 80%

• Behavioral economics and bounded rationality

  Behavioral economics and bounded rationality

  Ects : 3

  Lecturer :
  BERTRAND VILLENEUVE
  

  Total hours : 21

  Overview :

  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

  Require prerequisites :

  Expected utility. Basic game theory. Basic probability theory, in particular Bayesian calculus.

  Learning outcomes :

  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.

  Assessment :

  • MCQs all along the classes (30%).
  • Final written exam (70%).

  Bibliography-recommended reading

  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

  Lecturer :
  FARID TOUBAL
  

  Total hours : 21

  Overview :

  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)

  Recommended prerequisites :

  Solid knowledge in Microeconomics, Econometrics and International Trade

  Require prerequisites :

  Microeconomics, Macroeconomics, Econometrics, International Trade

  Learning outcomes :

  • 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.

  Assessment :

  Home works

  Bibliography-recommended reading

  • 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

  Lecturer :
  FABRICE ROSSI
  

  Total hours : 36

  Overview :

  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 6 (M2 Economie Internationale et Développement) 6 (M2 Diagnostic économique international)

  Require prerequisites :

  • 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.

  Learning outcomes :

  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

  Assessment :

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

  Introduction to Matlab programming

  Lecturer :
  INES MOURELON
  

  Total hours : 12
• Python for data science

  Python for data science

  Ects : 3

  Lecturer :
  MOHAMED KHALIL EL MAHRSI
  

  Total hours : 18

  Overview :

  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

  Recommended prerequisites :

  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.

  Require prerequisites :

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

  Learning outcomes :

  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

  Assessment :

  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

  Total hours : 24

  Coefficient : 2
• Machine Learning for Applied Economic Analysis

  Machine Learning for Applied Economic Analysis

  Ects : 3

  Lecturer :
  MATHILDE GODARD
  

  Total hours : 24

  Overview :

  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

  Recommended prerequisites :

  Python (beginner/intermediate), Machine Learning, Microeconometrics.

  Learning outcomes :

  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.

  Assessment :

  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).

  Bibliography-recommended reading

  • 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

  Lecturer :
  SYLVAIN CARRE
  

  Total hours : 18

  Overview :

  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

  Recommended prerequisites :

  Basic microeconomic knowledge, basic notions of calculus and optimization, basic notions of game theory.

  Learning outcomes :

  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.

  Assessment :

  Presentation 50% Written exam 50%

• Computational social choice

  Computational social choice

  Ects : 3

  Lecturer :
  JEROME LANG
  

  Total hours : 24

  Coefficient : 1
• Incremental learning, game theory and applications

  Incremental learning, game theory and applications

  Ects : 3

  Lecturer :
  MOHAMMED RIDA LARAKI
  

  Total hours : 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

  Lecturer :
  ELSA PERDRIX
  

  Total hours : 21

  Coefficient : 2
• Policies in developing countries

  Policies in developing countries

  Ects : 3

  Lecturer :
  OLIVIA BERTELLI
  

  Total hours : 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

  Lecturer :
  SYLVAIN CARRE
  

  Total hours : 18

  Overview :

  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

  Recommended prerequisites :

  Basic microeconomic knowledge, basic notions of calculus and optimization, basic notions of game theory.

  Learning outcomes :

  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.

  Assessment :

  Presentation 50% Written exam 50%

• Advanced environmental macroeconomics

  Advanced environmental macroeconomics

  Ects : 3

  Lecturer :
  GAUTHIER VERMANDEL
  

  Total hours : 15

  Coefficient : 2