syllabi M2 Stat_Eco - L`École d`économie de Toulouse

Transcription

Master 2 Economics & Statistics
2015-2016
SYLLABUS
Big Data
Survey Sampling
Time Series
Lifetime Analysis
Econometric Panels
Econometrics of Qualitative Variables
Marketing
Non Parametric Models
Linear Models
Statistical Consulting Workshop
Statistical Software
Life Insurance
Non Life Insurance
Spatial Econometrics
Geomarketing
Data Bases
Web Mining
Data Mining
Scoring
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Enseignant responsable
Autre(s) enseignant(s)
Volume horaire CM
Volume horaire TD
Volume horaire TP
Big Data
Master 2 Economics & Statistics/semester 1
6
S. GADAT
36
0
0
OBJECTIVES
This course provides a modern view of statistical methods for high dimensional estimation and classification, as well as
stochastic prediction. For each method, we will describe the underlying theoretical aspect with associated statistical and
numerical results. The course will take place in a computer room so that we can illustrate immediately the ideas covered in
lecture through simulated and real data sets. The course notes, data sets, exercises and their solutions, R and Matlab codes,
and other materials will be made available for the students.
COURSE OUTLINE
Reminders on Convex Optimization (1 session) High Dimensional regression (2 sessions) Stochastic Algorithms (1 session) High dimensional
Classification (1 session) Matrix completion (1 session)
REQUIREMENTS
R & Matlab programming knowledge
TEXT BOOKS
Bühlmann, P. and van de Geer, S. (2011). Statistics for High-Dimensional
Data: Methods, Theory and Applications. Springer.
GRADING POLICY
UE 1
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Survey Sampling
3
A.RUIZ-GAZEN
18
0
0
OBJECTIVES
Sampling theory course gives the basis for inference in a design based approach.
Different sampling designs and estimation methods are studied in detail.
COURSE OUTLINE
Chapter 1: Introduction
Chapter 2: Estimation of totals and means using the Horvitz-Thompson estimator for a simple random sampling with and without
replacement. Calculation of a sample size for a given precision.
Chapter 3. Estimation of totals and means using the Horvitz-Thompson estimator for a general sampling design. Bernoulli and
unequal probabilities with replacement survey designs. Design effect.
Chapter 4: Estimation of a ratio. Estimation by substitution of a ratio and estimation by linearization of its asymptotic variance.
Chapter 5: Stratified sampling. Allocation of the total sampling size to the strata. Horvitz-Thompson estimation and variance
estimation.
Chapter 6: Post-stratified estimation, estimation by ratio and estimation by regression. Estimation methods which take into account auxiliary
information. Link with calibration estimators.
REQUIREMENTS
TEXT BOOKS
GRADING POLICY
The course is assessed through an interrogation that takes place mid-course, a final exam and a project to hand in.
UE 2a
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Time Series
3
F.BARTHE
18
0
0
OBJECTIVES
The objective of this module is twofold : provide the students with a good understanding of the main probabilistic models used in the
statistic analysis of time series, and apply these models to concrete data.
COURSE OUTLINE
Times series analysis deals with the description, modeling and forecasting of data which are collected along time and enjoy strong
correlations between successive observations.
After reviewing the classical methods to extract the trend and seasonality of a time series, we will focus on the study of the
residual/noise term. We will introduce the main notions in the study of weakly stationary random processes (autocorrelation, partial
autocorrelation, spectral density, innovation). The AutoRegressive Moving Average processes will be presented, together with the
methods allowing to identify them from observations. Eventually, models with autoregressive conditional heteroskedasticity will be
considered, as they are employed commonly in modeling financial time series that exhibit time-varying volatility clustering.
The class also includes sessions on machines to implement all of these methods through the use of the R software
REQUIREMENTS
Probability and Statictics course of bachelor level. Basic knowledge on complex numbers.
TEXT BOOKS
• BROCKWELL P. & DAVIS R. « Introduction to time series and forecasting » Springer
• GOURIEROUX C. et MONFORT A. – « Cours de séries temporelles », Economica
• ARAGON Y. – « Séries temporelles avec R», Springer
More on: http://www.math.univ-toulouse.fr/~barthe/M2timeseries
GRADING POLICY
Final exam and short report to hand after the final session on machines.
UE 2b
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Lifetime Analysis
2, 5
E. LECONTE
18
0
0
OBJECTIVES
To recognize a situation where censored data appear and to be able to analyze such data by implementing an appropriate modelling.
All the courses take place in a computer room and an important place is given to the learning of the dedicated software (R and SAS).
COURSE OUTLINE
Survival distributions: specific functions and the most used distributions for survival data.
Censored data: censoring and truncation, right censoring.
Non parametric estimation of the survival function and the cumulative hazard function: Kaplan- Meier and Nelson- Aalen estimators.
Comparison of two or more survival distributions : the weighted logrank tests.
Parametric regression models: proportional hazards models and accelerated failure time models.
The semi-parametric Cox model: modelling, partial likelihood, estimation and tests of the parameters, estimation of the baseline
cumulative hazard, validation and extensions.
REQUIREMENTS
Inferential statistics: hypothesis testing, maximum likelihood estimation and tests (Wald, score and likelihood ratio tests).
TEXT BOOKS
Hill C., Com-Nougué C., Kramar C., Moreau T., O'Quigley J., Senoussi R. and Chastang C. (1990), Analyse statistique des données de
survie, Flammarion.
Klein J. P. and Moeschberger M. L. (1997), Survival Analysis - Techniques for censored and truncated data, Springer.
Allison P. D. (1995), Survival analysis using the SAS system. A practical guide. SAS Institute Inc.
GRADING POLICY
A final exam with a short theoretical part without documents and a longer part to analyse real data with computer software (R or SAS)
with all the documents allowed.
UE 3a
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Econometric Panels
2, 5
P. LAVERGNE
OBJECTIVES
Understand and be able to apply econometric methods for panel data.
COURSE OUTLINE
Background on linear regression
Panel data
Instrumental variables
REQUIREMENTS
Linear algebra, probability, statistical inference, basic econometrics.
TEXT BOOKS
Introduction to Econometrics by J. Stock and M. Watson Pearson Education
Principes d'Econometrie : traduction française J. Trabelsi
GRADING POLICY
Project 40%, final exam 60%
18
0
0
UE 3b
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Econometrics of Qualitative Variables
2, 5
L. BONNAL
18
0
0
OBJECTIVES
Construct an explanatory model for a qualitative variable or a truncated variable.
COURSE OUTLINE
Chapter 0: Review of qualitative variables, indicator and maximum likelihood estimation
Chapter 1: The simple qualitative model: logit and simple probit. Maximum likelihood estimation, marginal effects, odds ratio.
Chapter 2: The bivariate qualitative model. Estimation, problem identification, calculations ATE, ATT, ATNT.
Chapter 3: The multinominal qualitative model. Ordered model, unordered model, sequential model.
Chapter 4: The truncated or censored models: single and generalized Tobit.
REQUIREMENTS
Econometrics of linear models, statistical testing theory.
TEXT BOOKS
Econometrics analysis of cross section panel data years, Wooldridge, MIT press
Econometrics of qualitative variables, A Thomas, Dunod edition
Limited dependent and qualitative variables in econometrics, Maddala,
Econometric Society monographs.
GRADING POLICY
Final exam on machines.
UE 3c
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Marketing
2, 5
P. BIZZARI
18
0
0
OBJECTIVES
Courses on the operational aspects of statistical techniques in the context of marketing orientated data mining projects.
The purpose of this course is to raise awareness of the ins and outs of a project in a professional environment without
being exclusively focused in either the technical or theoretical part. This is illustrated in two stages via a presentation of
the main methodologies used (score, segmentation, decision tree.... etc). Then via a practical project in groups.
COURSE OUTLINE
Marketing relationship and CRM
Definitions and concepts, structure of information systems (data warehouse), data mining issues
Data mining mapping techniques
Statistics and data mining, application areas of data mining
Process of conducting a study
Conceptualisation, implementation, integration, measurement and monitoring
Data mining perspectives
Trends and issues of tomorrow
Case studies - Realisation of a project
Target marketing, customer segmentation, product associations, scoring attrition ("churn"), calculating customer value
REQUIREMENTS
TEXT BOOKS
GRADING POLICY
Final exam
UE 3d
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Non Parametric Models
2, 5
A.DAOUIA
18
0
0
OBJECTIVES
This course provides a modern view of the most popular nonparametric methods, especially on the important topics of density and
regression estimation in both univariate and multivariate cases. The basic idea of those methods is to let the data speak for themselves
without recourse to any a priori parametric specification. For each method, we will introduce the underlying theoretical aspects
(relevant mathematical results) although the proofs will be skipped. We will spend more time on cultural aspects (knowledge of the
methodology and interpretation of statistical results), computational aspects (implementation using R and Matlab softwares), and case
studies (returns of education, assets returns, etc). The course will take place in a computer room so that we can illustrate immediately
the ideas covered in lecture through simulated and real data sets. The course notes, data sets, exercises and their solutions, R and
Matlab codes, and other materials will be made available on the university's course website.
COURSE OUTLINE
Parametric versus nonparametric models
Nonparametric density estimation
Review of polynomial spline functions
Nonparametric regression estimation
Semi-parametric regression estimation
REQUIREMENTS
Traditional asymptotic statistics, Basics of R and Matlab programming.
TEXT BOOKS
Wasserman, L. (2006): All of Nonparametric Statistics. Springer
GRADING POLICY
Non parametric models: there will be an exam (50%) and a project (50%) in this class
UE 3e
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Statistical Consulting Workshop
5
36
0
0
OBJECTIVES
The statistical consulting class is not a classical course but rather a team project. Teams of four students and two teachers are
assigned a project topic given ny an outside pattern (firms, administrations, researchers in other fields) and work on the
project in a collaborative way from the beginning of October to the end of March.
COURSE OUTLINE
The team meets once a week to discuss the advancement of the project and presents regularily the results to the outside
member.
REQUIREMENTS
TEXT BOOKS
GRADING POLICY
An oral defense is organised at the end.
UE 3g
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Non Life Insurance
2,5
R. AIT‐MANSOUR
18
0
0
OBJECTIVES
Apporter les connaissances et les compétences de base en actuariat de l’assurance non-vie permettant aux étudiants de
poursuivre une carrière professionnelle en qualité de chargé d’études actuarielles, en compagnie d’assurance ou en
cabinet de conseil.
COURSE OUTLINE
1– Généralités sur l’assurance non-vie
Ce premier chapitre est consacré à une présentation rapide de l’assurance non vie et ses spécificités
2– La tarification
Ce second chapitre traite de la problématique de fixation d’un tarif pour un produit d’assurance non-vie: sur quels
critères se calcule un tarif? Comment établir la tarification?
3- Le provisionnement
Dans ce chapitre, la question de la constitution de réserves est abordée, tant pour répondre à une exigence
comptable que pour respecter les engagements pris envers les assurés.
4- La réassurance
Les divers dispositifs de réassurance sont abordés dans ce chapiteez: réassurance proportionnelle et réassurance non
proportionnelle.
5- Eléments de comptabilité des assurances
Dans ce chapitre, nous abordons les principes d'établissement des comptes d'assurance non-vie (bilan et compte
de résultat).
6- La solvabilité
Les régles prudentielles en viguer (norme Solvency 1) sont décrits dans ce chapitre. Les grandes orientations des
futures régles (Solvency II) sont abordées en fin de chapitre.
Les différents chapitres comportent des apllications numériques permettant d'illustrer les divers concepts abordés
dans le cours.
REQUIREMENTS
Cours de Statistiques et de Probabilités de Licence.
TEXT BOOKS
TOSETTI Alain et Al.– « Assurance, Comptabilité, Réglementation, Actuariat », Economica
DENUIT Michel et CHARPENTIER Arthur– « Mathématiques de l’assurance non-vie »,Economica
PETAUTON Pierre– « Théorie de l’assurance dommages », Dunod
GRADING POLICY
UE 7b
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Spatial Econometrics
2,5
A.RUIZ-GAZEN
T.LAURENT, N.DEBARSY
18
0
0
OBJECTIVES
The aim of this course is to introduce the statistical techniques adapted to the treatment of georeferenced data sets.
COURSE OUTLINE
The spatial dimension must be taken into account for modeling the inhomogeneity effects of a spatial process as well as its spatial
dependence structure. After describing the three types of spatial data (geostatistical, areal and point patterns), we focus on the case of areal
data which is more frequent in spatial econometrics. We present exploratory techniques dedicated to spatial data sets. We introduce specific
tools such as variograms, weight matrices, Moran index, etc. for studying the autocorrelation structure. Then we introduce the family of
spatial simultaneous autoregressive models (Durbin, spatial lag model, spatial error model). In the framework of these models, we discuss
estimation and testing problems, interpretation of coefficients and marginal effects, and prediction. If time allows, we briefly cover some
additional topics such as kriging and point patterns characteristics. The illustration of these techniques is done with R and the following
packages: spdep, GeoXp, geoR, spatstat.
REQUIREMENTS
Linear models and R programming
TEXT BOOKS
Applied Spatial Data Analysis with R
Roger S. Bivand, Edzer Pebesma and V. Gómez-Rubio
UseR! Series, Springer
GRADING POLICY
Project
UE 7c
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Geomarketing
2,5
A.RUIZ-GAZEN
A.LARA
18
0
0
OBJECTIVES
This course introduces the techniques and concepts used in geomarketing. Geomarketing is the integration of the spatial
dimension in the statistical marketing analyses.
COURSE OUTLINE
After defining the various concepts of trading areas, potential, we introduce the problems of turnover prediction and best
location problems. For the former, we discuss the various interaction models used for flow data (gravity model, Huff model,
Multiplicative competitive interaction model) and their estimation methods. For the latter, we review the literature on locationallocation
problems. An applied part of the course taught by a professional illustrates the treatment of a geomarketing case study.
REQUIREMENTS
R programming
TEXT BOOKS
GRADING POLICY
Project
UE 7d
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Data Bases
2,5
R. TOURNIER
18
0
0
OBJECTIVES
The objective of this course is to earn a basic knowledge on Decision Support Systems through database systems. This
course allows understanding how some computer tools may help a decision maker/manager to have a global vision of
what is happening within his institution or company. The course introduces software used in the domain and details the
use databases as well as On-Line Analytical Processing tools (also called OLAP tools). The course is an introduction to
more the complex environment of decision support systems.
COURSE OUTLINE
Introduction to decision support and decision support systems (as well as a word on pivot tables and Excel—the most
used tool);
Query languages for databases (SQL). Starting with simple queries to the more complex analytical queries (application
with Microsoft Access);
OLAP analytical tools, design of multidimensional databases and analytical reports (application with SAP Business
Objects).
REQUIREMENTS
None. Knowledge of databases and spreadsheets (Excel or Calc) may help.
TEXT BOOKS
Ralph Kimball books such as «The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling», 2008.
GRADING POLICY
Project done in pairs (40% of the evaluation) & final written exam (60% of the evaluation).
UE 7e
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Web Mining
2,5
Y.PITARCH
18
0
0
OBJECTIVES
The objective of the module « web mining » is two-fold: it will be firstly to draw the widest theoretical overview of “web
mining” and techniques conventionally used to search such data. Secondly, students will be confronted practically with this
knowledge extraction problem via the establishment of a TP/project.
COURSE OUTLINE
It is customary to divide this area into three sub-areas: web content search (web content mining) structures search (web
structure mining) and web usage search (web usage mining). For each of these categories, we describe the motivations, the
applications, some work of major research and conclude by mentioning some challenges.
Concerning the second part of the module, the objective is to allow students to experience all the knowledge extraction
process: from data collection to analysis and interpretation of analytical results. Practical work will therefore have a purpose
and students will be asked to provide a summary at the end of the module. Some basics of programming will be required
(mainly to retrieve data and pre-treat it). A little booklet will be made available to students to help them in this task. The programming
language used is yet to be determined but will probably be Perl. The software used for data analysis is not yet defined. At least
two solutions seem relevant: the use of R coupled with data mining module or use of composition workflow
software for extraction, pre-processing, processing, post-processing, data visualisation and analysis,
e.g. Knime or Orange.
REQUIREMENTS
TEXT BOOKS
Ralph Kimball books such as «The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling», 2008.
GRADING POLICY
UE 7f
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Data Mining
6
X. GENDRE
36
0
0
OBJECTIVES
The objective of this module is to introduce statistical methods in order to explore data structures. The extraction of
information is central to the new challenges introduced by access to ever larger databases and this set of methods finds its
interest in studies where potentially large scale data intervenes.
COURSE OUTLINE
In the first part of the course, we introduce the classic optimisation methods that are PCA and its variants (CA, MCA,
MDA,...). Our approach is intentionally general in order to illustrate the mathematical principles common to all these tools
which could easily be incorporated into a variety of settings. The second part of the course is an overview of methods
commonly used in the industry to explore and process data. We talk about ecision trees (CART for example), neutral
network (perceptrons, Kohonen networks), moving averages and ascending hierarchical classification.
This class also includes sessions on machines to implement all of these methods through the use of th eR software. In
addition, during these practice sessions, we will have the opportunity to introduce some more advanced points such as
model selection, cross-validation, the bootstrap...
REQUIREMENTS
TEXT BOOKS
GRADING POLICY
UE 8
2015-2016
Intitulé du cours
Niveau / Semestre
Crédits
Volume horaire CM
Volume horaire TD
Volume horaire TP
Scoring
6
36
0
0
OBJECTIVES
Learn the background of generalized linear models and the basic skills of supervised classification using logistic regression.
COURSE OUTLINE
Theoretical part.
After defining the scoring goals and vocabulary, the first chapter of the course is an introduction to the generalized linear
model. Then we cover extensively the logistic regression model. We detail the definition, estimation and interpretation of the odds ratios as
well as the computations of marginal effects. We discuss tools such as the Lorenz and ROC curves for the evaluation of the quality of the
model and the selection of a score threshold.
Applied part.
A professional discusses a scoring case study.
REQUIREMENTS
R and SAS programming
TEXT BOOKS
Extending the linear model with R, J.J. Faraway, Chapman& Hall/CRC, 2006.
J.M. Hilbe, Logistic regression models, CRC Press, Chapman and Hall, 2009.
R. Anderson, The credit scoring toolkit, Oxford U.P., 2007.
GRADING POLICY
Exam and project.
UE 9

syllabi M2 Stat_Eco - L`École d`économie de Toulouse

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