Data-Driven Decisions and Digital Economics - 297 - 2ème année de Master

Data Analytics - 12 ECTS

• Machine Learning

  Machine Learning

  Ects : 6

  Enseignant responsable :
  

  • FABRICE ROSSI

  Volume horaire : 36

  Description du contenu de l'enseignement :

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

  1. general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
  2. machine learning as automated program building from examples (running example: decision trees)
  3. machine learning as optimization:
     1. empirical risk minimization
     2. links with maximum likelihood estimation
     3. surrogate losses and extended machine learning settings
     4. regularisation and kernel methods (support vector machines)
  4. reliable estimation of performances:
     1. over fitting
     2. split samples
     3. resampling (leave-one-out, cross-validation and bootstrap)
     4. ROC curve, AUC and other advanced measures
  5. combining models:
     1. ensemble techniques
     2. bagging and random forests
     3. boosting
  6. unsupervised learning:
     1. clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)
     2. 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 file
    • recoding 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 statistics
    • conditional probabilities and conditional expectations
    • core 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
• Time Series and Anomaly Detection

  Time Series and Anomaly Detection

  Ects : 3

  Enseignant responsable :
  

  • MADALINA OLTEANU

  Volume horaire : 24

  Description du contenu de l'enseignement :

  This lecture is thought as an introduction to the analysis of complex data, and particularly to that having a temporal component. Methods aimed at exploring and modelling time series, longitudinal data and graphs with temporal components will be addressed. The issues of detecting patterns, breakpoints, changes of regimes, and anomalies will be at the core of the different approaches.

  The first chapters will be devoted to hidden Markov models. After having briefly recalled some definitions and properties of Markov processes, we will define hidden Markov processes, illustrate them with several examples and give some of their properties. Inference techniques using the EM algorithm and Bayesian approaches will be presented and illustrated in practice. We will particularly focus on some specific models which are extremely useful for segmenting time series stemming from the economics field, such as autoregressive Markov switching models.

  The second part of the lecture will tackle the issue of change-point detection methods. We will start by introducing the change-point detection issue. More specifically, we will consider several frameworks and derive inference procedures for computing and locating change-points : online vs. offline strategies, single vs. multiple change point detection, known vs. unknown number of change points, parametric vs. non-parametric approaches.

  The third chapter will be aimed at introducing the issue of anomaly detection in the context of temporal data. After having defined what an anomaly is, we will start by assessing whether and how hidden-Markov models and change-point analysis may be useful for detecting anomalies. Then, we will compare these two approaches with other techniques, stemming either from the field of computational statistics, or from that of machine learning. During this chapter, we will also consider the questions of detecting patterns and clustering temporal data.

  The fourth chapter will address data that can be modelled as a graph or a temporal graph. We will start by introduce some definitions and summaries for characterising the network (degree distribution, centrality indices, ...). Afterwards, we will tackle the questions of community detection and graph clustering. Eventually, we will address the issues of random networks and associated tests for randomness. Models and methods introduced in this lecture will be practiced using existing implementations in R and “ real-life ” datasets.

  Coefficient : 0,5

  Pré-requis obligatoire :

  Students are expected to have some notions of probabilities, statistical inference theory and time series analysis (ARIMA models). An intermediate

  knowledge of R and/or Python is also desirable.

  Compétences à acquérir :

  Gain some background and perspective of time series analysis from a data science point of view.

  Be able to handle temporal data subject to anomalies and change-points.

  Have some basic knowledge about graphs and temporal graphs mining.

  Mode de contrôle des connaissances :

  Data challenge. A project to be done individually or by two, analysing real life data.

• Data Science Project

  Data Science Project

  Ects : 3

  Volume horaire : 18

  Coefficient : 0,5
• Machine Learning

  Machine Learning

  Ects : 6

  Enseignant responsable :
  

  • FABRICE ROSSI

  Volume horaire : 36

  Description du contenu de l'enseignement :

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

  1. general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
  2. machine learning as automated program building from examples (running example: decision trees)
  3. machine learning as optimization:
     1. empirical risk minimization
     2. links with maximum likelihood estimation
     3. surrogate losses and extended machine learning settings
     4. introduction to deep learning
  4. reliable estimation of performances:
     1. over fitting
     2. split samples
     3. resampling (leave-one-out, cross-validation and bootstrap)
     4. ROC curve, AUC and other advanced measures
  5. combining models:
     1. ensemble techniques
     2. bagging and random forests
     3. boosting
  6. machine learning for causal inference

  Coefficient : 1

  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 file
    • recoding 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 statistics
    • conditional probabilities and conditional expectations
    • core 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
  • 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

Digital Economics - 18 ECTS

• Competition and network economics

  Competition and network economics

  Ects : 3

  Enseignant responsable :
  

  • ANTOINE CHAPSAL
  • ANNA CRETI

  Volume horaire : 30

  Description du contenu de l'enseignement :

  Theory and practice of competition in network industries; antitrust issues; theory of network and network effects; two-sided platforms and pricing

  Coefficient : 0,5

  Pré-requis recommandés :

  Industrial Organization

  Pré-requis obligatoire :

  Advanced Micro

  Compétences à acquérir :

  Understanding of competition and regulation issues in network and digital economics

  Mode de contrôle des connaissances :

  Written exam

  Bibliographie-lectures recommandées

  Belleflamme-Peitz, Industrial Organization

• Blockchain economics

  Blockchain economics

  Ects : 6

  Enseignant responsable :
  

  • LOUIS BERTUCCI

  Volume horaire : 36

  Description du contenu de l'enseignement :

  While this is a fairly recent technology, this class will take students through the fundamentals of blockchains as well as implications regarding financial, economic or social interactions. The class will start by some history needed to understand what lead to the creation of Bitcoin, the first blockchain, in 2009. We will then review the detailed functioning of a blockchain. We will continue by discussing important current developments in the industry as well as implications for the economic environment. Lastly, we will discuss potential future developments and how blockchains will impact a broad range of industries. Students will also be introduced to recent academic work related to blockchains.

  Students will be asked to pick a blockchain project from a list and present it briefly during the presentation session in front of the class (group presentation). When reaching this presentation session, students will be expected to be able to assess the pros and cons of a given blockchain project, and have a critical opinion on this project.

  Coefficient : 1

  Pré-requis recommandés :

  The first prerequisite is coding. Knowledge in Python and/or Javascript will greatly help students perform the homework. Student less familiar with Python are expected to increase their Python skills by the end of the semester.

  The second prerequisite is basic economics (competition, market economy, utility maximization).

  While knowledge in computer science and economics is needed to properly understand what a blockchain is, we will go through what is needed just to make sure everyone is on the same page. In particular we will go through asymmetric cryptography, distributed networks, consensus, game theory, financial markets and corporate finance. Although students with knowledge in any of those topics will be more confortable, I intend to present them “from scratch”.

  Compétences à acquérir :

  The objective of this class is to give students a deep theoretical overview of what a blockchain is. Nonetheless we will also use mock-blockchains, write smart contracts and interact with them, through some computer sessions. This will help solidify the knowledge learned and de-mystify the functioning of a blockchain.

  Students will gain a deep knowledge of how a blockchain works internally. They will also be very aware of the different issues and perhaps they will be able to spot new use cases for a blockchain.

  Mode de contrôle des connaissances :

  The evaluation is composed of a group presentation (1/3), homework (1/3) and a final exam (1/3). Class participation can be highly rewarded especially for students who struggle with homework. Students are encouraged to actively interact during the class.

  Bibliographie-lectures recommandées

  Melanie Swan, Blockchain: Blueprint for a new economy, O’Reilly, 2015

  Andreas Antonopoulos, Mastering Bitcoin, 2nd edition, O’Reilly, 2017

  Andreas Antonopoulos / Gavin Wood, Mastering Ethereum, 1st edition, O’Reilly, 2018

  Primavera De Filippi/ Aaron Wright, Blockchain and the Law: The Rule of Code, Harvard University Press, 2018

• Financial Data et Systemic risk

  Financial Data et Systemic risk

  Ects : 3

  Enseignant responsable :
  

  • Sylvain BENOIT

  Volume horaire : 24

  Description du contenu de l'enseignement :

  The course will equip students with the necessary knowledge to be able to undertake econometric analysis of the type commonly associated with modern financial econometrics research. Substantial emphasis will be placed on the development of programming skills in Python (or in MATLAB, especially for financial contagion and multivariate analysis).

   

  Course outline:

  1. Data collection (CRSP-Compustat, Yahoo-Finance, ECB data warehouse)
  2. Market Risk Measurement (Value-at-Risk, Expected Shortfall) – ARCH/GARCH models – univariate time series
  3. Backtesting tests for market-risk measurement (independence test, unconditional coverage test, conditional coverage test, super exception)
  4. Systemic Risk and Macroprudential regulation (SIFIs identification, MES, SRISK, ?CoVaR) – multivariate time series
  5. Principal Component Analysis (absorption ratio computation)
  6. Contagion models (direct and indirect effects decomposition)

  Coefficient : 0,5

  Pré-requis recommandés :

  Time Series Analysis. Python programming.

  Compétences à acquérir :

  The course provides a deep knowledge of the advanced time series techniques and their application to systemic risk. A technical presentation of these models will be given, before studying applications of these models to systemic risk.

  Mode de contrôle des connaissances :

  Individual homework assignment.

  Bibliographie-lectures recommandées

  Benoit, S., Colliard, J.-E., Hurlin, C. and C. Pérignon (2017) Where the Risks Lie: A Survey on Systemic Risk, Review of Finance, 21(1), 109-152.

  Benoit, S., Hurlin, C. and C. Pérignon (2019) Pitfalls in Systemic-Risk Scoring, Journal of Financial Intermediation, 38, 19-44.

  Campbell, S. D. (2004) A Review of Backtesting and Backtesting Procedures, Working paper, Federal Reserve Board.

  Christofferson, P. and Pelletier, D. (2004) Backtesting Value-at-Risk: A Duration-Based Approach, Journal of Financial Econometrics, 2(1), 84-108.

  Du, Z. and J. C. Escanciano (2015) Backtesting Expected Shortfall: Accounting for Tail Risk, Management Science.

  Diebold, F.X. and K. Yılmaz (2009) Measuring Financial Asset Returns and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(1), 158-171.

  Diebold, F.X. and K. Yılmaz (2012) Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers, International Journal of Forecasting, 28(1), 57-66.

• Private Cryptocurrencies

  Private Cryptocurrencies

  Ects : 3

  Enseignant responsable :
  

  • ROMAIN PLASSARD

  Volume horaire : 21

  Coefficient : 0,5
• Experimental Economics

  Experimental Economics

  Ects : 3

  Enseignant responsable :
  

  • CLAIRE RIMBAUD

  Volume horaire : 21

  Description du contenu de l'enseignement :

  The module will cover both methodology - why and how experiments in economics are conducted - and specific topics from the experimental literature via recent research articles.

  Coefficient : 2 pour le M2 296 et 0,5 pour le M2 346

  Compétences à acquérir :

  The aim of the module is to introduce students to the use of experimental methods in economics.

  Bibliographie-lectures recommandées

  Charness, G., & Pingle, M. (Eds.). (2021). The art of experimental economics: twenty top papers reviewed. Routledge.

  Friedman, D., & Sunder, S. (1994). Experimental methods: A primer for economists. Cambridge university press.

  Moffatt, P., Starmer, C., Sugden, R., Bardsley, N., Cubitt, R., & Loomes, G. (2009). Experimental economics: Rethinking the rules. Princeton University Press.

  + articles cited in class.

Data Analytics - 12 ECTS

• NLP for economic decisions

  NLP for economic decisions

  Ects : 3

  Volume horaire : 24

  Coefficient : 2 pour le M2 296 et 0,5 pour le M2 346
• Machine Learning for Economists

  Machine Learning for Economists

  Ects : 3

  Enseignant responsable :
  

  • Mathilde GODARD

  Volume 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 pour le M2 296 et 0,5 pour le M2 346

  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, and its limitations.

  3. Think of unstructured data (text, image) as data we can use when economic outcomes are missing.

  4. Specific focus on application of ML to social policies (health/labor/taxation/environment etc.).

  Mode de contrôle des connaissances :

  Grading:

  1. In-class pairwise presentation of an academic paper (30% of overall grade).

  2. Final exam (in-class written text). 70% 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
• Neural Networks

  Neural Networks

  Ects : 3

  Enseignant responsable :
  

  • JOSEPH RYNKIEWICZ

  Volume horaire : 18

  Coefficient : 0,5
• Data visualisation

  Data visualisation

  Ects : 3

  Volume horaire : 15

  Coefficient : 0,5

Digital Economics - 9 ECTS

• Platform economics

  Platform economics

  Ects : 3

  Volume horaire : 18

  Coefficient : 0,5
• Solidity and smart contract development

  Solidity and smart contract development

  Ects : 3

  Enseignant responsable :
  

  • TIANCHAN DONG

  Volume horaire : 18

  Description du contenu de l'enseignement :

  The course begins with an introduction of Github and Solidity coding fundamentals before diving into smart contract development. Participants will learn the most common ERC standards for Tokens and NFTs before building more complex contracts for DAOs. Along the way, blockchain architecture and the revenue model of the Ethereum protocol will also be explored.

  The course schedule is as follows:

  Week 1 - Ethereum history, the EVM, and Remix

  Week 2 - Solidity Concepts and Tokenomics

  Week 3 - DeFi and MEV

  Week 4 - NFTs and Use Cases

  Week 5 - DAOs

  Week 6 - Advanced concepts: Memory and Security

  Coefficient : 0,5

  Pré-requis recommandés :

  Familiarity with Remix IDE.

  Open source collaboration, especially Github.

  Compétences à acquérir :

  At the conclusion of this course, participants will gain a deep understanding of blockchain architecture and programming, executing the most common use cases such as NFT and DAOs. The course will also touch upon open source communities, its valuation and community management.

  Mode de contrôle des connaissances :

  The level of mastery will be continuously assessed throughout the course by:

  1. A weekly presentation on a topic more in depth than what is presented in the lecture material
  2. Weekly homeworks
  3. Final oral exam

  Bibliographie-lectures recommandées

  Solidity Documentation - docs.soliditylang.org/en/latest/ Code examples - solidity-by-example.org

• Empirical Industrial Organization

  Empirical Industrial Organization

  Ects : 3

  Enseignant responsable :
  

  • Daniel HERRERA ARAUJO

  Volume horaire : 21

  Coefficient : 2 pour le M2 296 et 0,5 pour le M2 346

Job Market Insertion - 9 ECTS

• Business Cases

  Business Cases

  Enseignant responsable :
  

  • RENE AID

  Volume horaire : 24

  Description du contenu de l'enseignement :

  The lecture is a sequence of use-cases in the industry and in the service business performed by professionals.

  Coefficient : Validation

  Compétences à acquérir :

  Knowing some of the most common use cases of data sciences in firms decision making business.

  Mode de contrôle des connaissances :

  Presentation of a resume of the lectures before a jury.

• Communication

  Communication

  Volume horaire : 12

  Coefficient : Validation
• Internship

  Internship

  Ects : 9

  Description du contenu de l'enseignement :

  Students must intern at a company (semester 4) for at least 4 months.

  Coefficient : 1,5

  Compétences à acquérir :

  The students should also complete an end-of-studies internship lasting at least 4 months. The curriculum includes guest lectures by visiting professionals on issues related to Big Data, providing another means of connecting with relevant business circles.

  Mode de contrôle des connaissances :

  The internship will conclude with a report to be reviewed by a committee.