Introduction to Econometric Software

Module fact file

Masters Level
Autumn Term
10 credits
Contact Hours:
14:00-18:00 (Over four days)
MA Social Research
Available to graduate students who have taken Data Analysis or equivalent.

Contact details

Module lead
William Pouliot
Shixuan Wang

Module description

This course will take a practical hands on approach to introducing students to three main econometrics software packages along with a word processing software package and a popular code repository platform. In particular, the course will consider Stata in Part 1, EViews in Part 2, LATEX (in particular - TEXworks) and GitHub in Part 3 and finally MATLAB in Part 4.

The first two packages are what are called front-end packages, which are menu driven and relatively user-friendly, allowing the user to carry out demanding econometric techniques at the click of a button. However, both also have powerful programming elements, which we will briefly explore. In particular, we will use EViews to focus on time series/financial econometrics, and Stata to consider cross sectional econometrics. Part 3 will focus on a powerful word processing package that enables users to easily create professional looking .pdf documents for writing up empirical results, and a platform where they can store all of their econometric coding work for public access and replication. The final Part 4 of the module will briefly introduce MATLAB, a multi-paradigm numerical computing environment and fourth-generation programming language.

All four packages are heavily used by academics and hence are important foundations for postgraduate research students, whether they are part of their initial research agenda or otherwise. The importance of making code files available for replication, especially in economics, cannot be understated, especially considering the Reinhart and Rogoff controversy.


Data Analysis, introductory statistics or a basic quantitative methods course.  Having used a statistical software package such as Stata or SPSS previously is not necessary, but would be considered an advantage.

Learning outcomes

On completion of the module, students should be able to:

  • Import data from a variety of sources, suitably transform and adjust it and subsequently plot high quality graphs suitable for publication.
  • Run simple OLS estimations and diagnostic checks.
  • Use estimated equations to make forecasts.
  • Utilize slightly more advanced techniques such as IV and panel estimation.
  • Design basic programs for repetitive tasks.
  • Use a range of different MATLAB toolboxes for a various tasks and estimations.
  • Present all of the above in a format suitable for publication.
  • Make available your code files in an academically professional manner.


Following the completion of the sessions, the students will be given a choice of three assessments: either a time series, a cross-section, or a more programming orientated exercise (to be done in either EViews, Stata or MATLAB respectively). The assessment is to be written up in the LATEX document preparation system, and all supporting programming files must be hosted on GitHub as a requirement of the course. Submissions are to be no longer than 2,000 words, and will account for 100% of the assessment.

Related degrees:

The optional modules listed on the website for this programme may unfortunately occasionally be subject to change. As you will appreciate key members of staff may leave the University and this necessitates a review of the modules that are offered. Where the module is no longer available we will let you know as soon as we can and help you make other choices.