Matemáticas básicas para estadística y econometría

Matemáticas básicas para estadística y econometría

Este completo curso introductorio de matemáticas está diseñado para dotar a los estudiantes de los conocimientos y habilidades fundamentales necesarios para destacar en cursos de econometría, incluyendo cálculo y álgebra lineal. Mediante una instrucción rigurosa, los estudiantes desarrollarán una comprensión profunda de los conceptos matemáticos y sus aplicaciones.

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350,00 €
Pago seguro y garantizado
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2 días
En línea a través de Teams
Stata

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

Participants are expected to have:

  • A basic knowledge of econometrics or statistics (e.g. regression analysis, causal inference concepts such as treatment effects)
  • Familiarity with Stata (data management, estimation commands, basic programming is a plus but not required)
  • Some exposure to policy evaluation or impact analysis is helpful but not mandatory

No prior knowledge of Optimal Policy Learning is required. The course will introduce all key concepts from first principles, while progressively moving toward more advanced applications.

Reccomended Pre-Course Reading:
  • Athey, S., & Wager, S. (2021). Policy Learning with Observational Data. Econometrica, 89(1), 133–161.
  • Cerulli, G. (2023). Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata. Springer.
  • Cerulli, G. (2025). Optimal Policy Learning Using Stata. The Stata Journal, 25(2), 309–343.
  • Cerulli, G. (2026). Optimal Policy Learning with Observational Data in Multi-Action Scenarios: Estimation, Risk Preference, and Potential Failures. International Journal of Data Science and Analytics, forthcoming.
  • Kitagawa, T., & Tetenov, A. (2018). Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice. Econometrica, 86(2), 591–616.
  • Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning. Proceedings of the National Academy of Sciences, 116(10), 4156–4165.
  • Manski, C. F. (2004). Statistical Treatment Rules for Heterogeneous Populations. Econometrica, 72(4), 1221–1246.
  • Zhou, Z., Athey, S., & Wager, S. (2023). Offline Multi-Action Policy Learning. Operations Research, 71(2), 564–580.

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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