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The University of Southampton

ECON2007 Econometrics with Big Data

Module Overview

The module will proceed from a review of known content (matrix algebra, linear regression, hypothesis testing) to more advanced topics such as multiple linear regression, heteroskedasticity, restrictions in hypothesis testing, issues of model misspecification, and an introduction to asymptotic theory and time series models to exploit large panel datasets for statistical inference. The module will thus equip students with fundamental methods for statistical inference on large panel datasets.

Aims and Objectives

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Demonstrate knowledge and understanding of analytical methods and the statistical tools for economic, specifically econometric analysis, in particular the assumptions and basic theory of classical linear regression how to deal with violations of standard assumptions of classical linear regression.
  • Demonstrate knowledge and understanding of statistical methods for analysing large datasets using traditional (classic regression) and algorithmic (numerical) methods.
  • Use data for statistical inference on the quantitative or qualitative workings of economic mechanisms and policies by setting up statistical tests.
  • Use quantitative reasoning in economic contexts and analyse and interpret data using adequate computer software, by performing basic regression programming in an econometric package and be able to evaluate regression estimates


The module explains the underlying statistical concepts, and establishes some of the formal results to understand the theoretical basis for regression. By using the STATA (or equivalent) econometric package, and interpreting its results, you will learn to demonstrate knowledge of the appropriate econometric methods

Learning and Teaching

Teaching and learning methods

Lectures, masterclasses (tutorials), computer lab sessions.

Independent Study122
Total study time150

Resources & Reading list

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013 ).  An introduction to Statistical Learning: With Application in R, . 

Introductory Econometrics: A Modern Approach. 

Econometric Analysis: 5th Edition .

Introductory Econometrics: A Modern Approach: 4th edition .

Introduction to Econometrics: 3rd edition .

An introduction to Statistical Learning: With Application in R.

Econometric Analysis. 

Introduction to Econometrics. 


Assessment Strategy

Assessment in this module is through coursework in form problem sets (50% of the final mark) and an end of semester examination (50%). The coursework both forms your ideas, and assesses your ability to apply econometric methods. The examination measures your grasp of the theory, and of the detailed rationale for the econometric methods.


MethodPercentage contribution
Coursework 50%
Examination 50%


MethodPercentage contribution
Examination 100%


MethodPercentage contribution
Examination 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Prerequisites: ECON2006 or MATH2011

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