Mathématiques de base pour les statistiques et l'économétrie

Mathématiques de base pour les statistiques et l'économétrie

Ce cours complet d'introduction aux mathématiques est conçu pour doter les étudiants des connaissances et compétences fondamentales nécessaires pour exceller en économétrie, notamment en calcul différentiel et intégral et en algèbre linéaire. Grâce à un enseignement rigoureux, les étudiants développeront une compréhension approfondie des concepts mathématiques et de leurs applications.

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2 jours
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Overview

This comprehensive two-day introductory mathematics course equips students with essential mathematical tools required for success in econometrics. With a focus on both linear algebra and calculus, students will gain the theoretical grounding and problem-solving skills needed to confidently tackle statistical modelling and data analysis.

Course Aims & Objectives
  • Provide foundational knowledge in key mathematical areas including calculus and linear algebra.
  • Prepare students to engage with advanced econometric techniques such as regression analysis and maximum likelihood estimation.
  • Develop mathematical reasoning skills applicable across economics, statistics, and quantitative research.
Key Skills Acquired

By the end of the course, students will understand:

  • Systems of linear equations and solution methods.

  • Matrix operations, transposition, determinants, and inverses.

  • Vector spaces, eigenvalues, and quadratic forms.

  • Calculus basics: derivatives, differentials, concavity/convexity.

  • Techniques in unconstrained optimisation for functions of a single variable.

Learning Outcomes
  • Mathematical Foundations: Gain essential knowledge in algebra and calculus to support the study of econometrics.
  • Proficiency in Mathematical Techniques: Understand and apply key mathematical tools used in econometric analysis.
  • Quantitative Skills: Develop skills in handling data, constructing models, and interpreting mathematical results.
  • Critical Thinking: Apply logical reasoning and structured problem-solving approaches to real-world economic problems.
Course Structure

Delivery Format: Two-Day Intensive

  • Lectures: 4 sessions (2 hours each)
  • Tutorials/Workshops: 2 sessions (1 hours each)

Agenda

Day 1:

Lecture 1: Linear Systems, Matrices & Operations
  • Systems of linear equations
  • Solving systems: substitution, elimination
  • Matrix notation & basic operations
  • Transposition
Tutorial 1: Hands-on applications
  • Solving economic models using matrix equations (e.g., input-output analysis)
  • Real-time problem-solving using short exercises

 

Lecture 2: Determinants, Inverses & Eigenvalues
  • Determinants and properties
  • Inverse of 2.2 and 3.3 matrices
  • Cramer’s Rule
  • Intro to eigenvalues & diagonalisation (essential conceptual grounding)
Tutorial 2: Applications
  • Working with matrix inversion in economic models
  • Identifying stability via eigenvalues in simple dynamic systems
Day 2:

Lecture 3: Calculus Basics and Differentiation
  • Derivatives and differentials
  • Rules of differentiation
  • Concavity, convexity, inflection points
  • Taylor expansion and Mean Value Theorem
Tutorial 3: Applications
  • Marginal analysis
  • Cost, revenue and profit functions
  • Approximating functions with Taylor Series
Lecture 4: Optimisation Techniques
  • First and second-order conditions
  • Unconstrained optimisation
  • Optimisation over closed intervals
Tutorial 4: Applications of Optimisation
  • Maximising/minimising utiity and profit functions
  • Economic interpretation of first and second-order conditions
  • Real-world problems using optimisation (e.g cost minimisation, revenue maximisation)
  • Introduction to interpreting mathematical results in the context of econometric models (e.g. MLE intuition via univariate log-likelihoods)

Prerequisites

There are no specific prerequisites to attend the course but we reccomend viewing the below pre-course reading

Reccomended Reading

 

Main Texts 

  • Hoy, M., Livernois, J., McKenna, C., Rees, R., & Stengos, T. (2011). Mathematics for Economics (3rd ed.). MIT Press. 

 

Students may also find the following useful as further reading. 

  • Chiang, A. C. (1984). Fundamental Methods of Mathematical Economics (3rd ed.). McGraw-Hill. 
  • Pemberton, M., & Rau, N. (2015). Mathematics for Economists: An Introductory Textbook. Manchester University Press. 
  • J.M. Wooldridge (2019) Introductory Econometrics: A Modern Approach, CENGAGE Learning Custom Publishing; 7th edition. 

Course Timetable

Subject to minor changes
Day Morning Session Afternoon Session
Day One 10am-1pm (London time) 2pm-5pm (London time)
Day Two 10am-1pm (London time) 2pm-5pm (London time)

Terms

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.
  • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
  • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
  • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

 

The number of delegates is restricted. Please register early to guarantee your place.

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