Basic Mathematics for Statistics and Econometrics

Basic Mathematics for Statistics and Econometrics

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360,00 €
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2 Days
Online via 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

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.

Delivered By

Student Testimonials

Giovanni's delivery is fantastic; makes great connections between new and prior knowledge and focuses on the key strengths and limitations of the discussed methods. Excellent course design that builds on the Introductory Machine Learning course and knowledge acquired in the PhD Econometrics sequences of courses. This is all nicely supplemented by detailed Stata code with explanations and sample datasets. 

Georgi Boichev

A student on our

Excellent course and great explanations on ML techniques and applications from Giovanni ! I leanred so much including the coding and applications plus the fundamentals of ML.

Marco Delprado

A student on our

The 'Advanced Machine Learning (AML)' experience was excellent for trying to gain more experience in Statistics using links Python and STATA.  

I'm not a Statistician! However, Giovanni managed to link the 'Fundamentals of Machine Learning (FML) ' to 'Advanced Machine Learning' in his usual excellent way. When starting the AML, for me I am pleased that the FML was a tremendous help and allowed me to use my mathematical knowledge for Physics and Science. I'm looking forward to Giovanni's next course (using large datasets) and his book.

Linking my knowledge of mathematics (from Science and Engineering) to Statistics. I do hope it is leading towards becoming better at 'Medical Statistics' that require very large datasets...and a big thank you to Giovanni!

William Ware

A student on our

Very well organized, very useful and relevant content, looking forward to joining future events!

Hebatallah Nashaat

A student on our

As always great service and real good courses. In addition, thanks to Professor Cerulli for making himself understood in the best way.

David Pineda

A student on our

The delivery of this course was exceptionally well done. It really helped me to appreciate the concepts as well as the practical applications in Stata. If you are new to this topic, this will provide a good introduction to complex issues.

Very easy to communicate, all emails contained all the information necessary. I think that the course was very well structured and organized. The tutor provided a number of codes that were extremely helpful for understanding. Overall, very useful and easy to follow!

I highly appreciated Professor Giovannu Cerulli course. The classes notes are very clear   and well prepared with an extensive coverage of the course subjects. And they are simultanesouly quite objective by focusing on the most important contents. Professor Giovannu Cerulli lectures are very didatic which greately helps the easily assimilation of the   corespondent knowledge. Furthermore, the course materials are quite   comprehensive and they englobe not only the classes notes, but also the referenced papers as well as data and Stata programs to estimate the models in this software. All in all, I greatly recommend this   course, as it really amazingly speeds up the acquaintance of the underlying theory and appied aplication in a very short period of time.

I found the Stata Summer School 2021 very useful and interesting. The course was perfectly structured and organised, with a good progression during the week. The instructors presented the topics covered in an easy and understandable way. There were room for questions and answers when needed. Materials shared for the course were tidy and informative, and I am sure I will use them frequently. This course was arranged online, which in my opinion worked very well. I believe the course delivered as promised and according to information found online when I signed up for the course. Easy to purchase/sign up for the course. User friendly. Quick and timely response.

Very efficient in terms of communication and delivery. Provides a very comprehesnive applied knowledge of stata. I would definitely recommend others to buy from them.

I went UK University of Cambridge for a summer school with Timberlake, it was excellent.

It was a great course and I thoroughly enjoyed it. Many of my fellow participants were eager to share their ideas. I thought the course could help further many people in a similar stage to my career!