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The University of Southampton
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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, heteroscedasticity, 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.

One of the pre-requisites for ECON3031

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

Syllabus

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

.

TypeHours
Independent Study122
Teaching28
Total study time150

Resources & Reading list

Introductory Econometrics: A Modern Approach. 

Econometric Analysis. 

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

Introduction to Econometrics. 

Assessment

Assessment Strategy

80% examination (2 hour), 20% coursework (2 pieces at 10% each). 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.

Summative

MethodPercentage contribution
Coursework 20%
Exam  (2 hours) 80%

Repeat

MethodPercentage contribution
Exam  (2 hours) 100%

Referral

MethodPercentage contribution
Exam 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Prerequisites: ECON2006 or MATH2011

Pre-requisites

To study this module, you will need to have studied the following module(s):

CodeModule
MATH2011Statistical Distribution Theory
ECON2006Statistical Theory 2
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