Mathématiques de base pour les statistiques et l'économétrie

Mathématiques de base pour les statistiques et l'économétrie

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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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