EC226: Econometrics 1
Jeremy Smith
Module Leader
Introduction
This module provides students with a thorough understanding basic principles of econometrics. You will be exposed to a range of different econometric tools. You will gain an understanding of simple OLS, the limitations of the application of OLS, potential alternative estimators for the different type of data one might encounter including: cross-sectional data sets, time series data set and panel data sets.. You will gain skills and techniques to analyse problems from an intuitive, graphical and statistical perspective applying your knowledge to real world data.
Principal Aims
The course aims to provide students with important skills, which are of both academic and vocational value, being an essential part of the intellectual training of an economist and also useful for a career. In particular the course aims to equip students with the following competencies: 1. An awareness of the empirical approach to economics; 2. Experience in the analysis and use of empirical data in economics; 3. Understanding the nature of uncertainty and methods of dealing with it; 4. The use of econometric software packages as tools of quantitative and statistical analysis.
Principal Learning Outcomes
Acquired the tools of quantitative and data science methods necessary to study optional second and third year modules offered in economics , including regression, regularisation, prediction, and model evaluation .
Developed their understanding of statistical (econometric) software and economics databases.
Further developed their communication skills in presenting and analysing data.
Developed further their techniques of statistical and data-driven methods; generated a thorough understanding of the econometrics and basic machine learning techniques, including a critical appreciation of their assumptions, limitations, and appropriate use.
Syllabus
The module will typically cover the following topics: Linear regression model. Least squares estimation. Dummy variables. Linear Restrictions. Classical Linear Regression Model Assumptions. Breakdown of CLRM assumptions. Errors in variables. Heteroscedasticity and implications for OLS. Structural change. Incorrect functional form and implications for OLS. Instrumental variable estimation. Limited dependent variable models. Panel data models. Model evaluation and prediction. Basics of supervised learning. Dynamic models with lagged dependent variable. Serial Correlation and implications for OLS. Types of autocorrelation. Nonstationarity and Cointegration.
Context
- Core Module
- L100 - Year 2, L1PA - Year 1, LM1D (LLD2) - Year 2, R9L1 - Year 2, R4L1 - Year 2, R2L4 - Year 2, R1L4 - Year 2, L1N2 - Year 2
- Optional Core Module
- GL11 - Year 2, GL12 - Year 2, V7MR - Year 2, R3L4 - Year 2
- Optional Module
- GL12 - Year 4, V7ML - Year 3, V7MP - Year 3, V7MM - Year 4
- Pre or Co-requisites
Any of:
EC139-15 Mathematical Techniques A AND EC124-15 Statistical Techniques B OR
EC140-15 Mathematical Techniques B AND EC124-15 Statistical Techniques B OR
IB122-15 Business Analytics (for WBS students) OR
EC106-30 Introduction to Economics OR EC107-30 Economics 1 for GL11, MORSE and other students from the
Mathematics/Statistics Department
Summary:Modules: (EC140-15 and EC124-15) or IB122-15 or (EC106-24 or EC107-30) or (EC139-15 and EC124-15)
- Restrictions
- May not be combined with modules EC203-30
Assessment
- Assessment Method
- Coursework (40%) + Centrally-timetabled examination (On-campus) (60%)
- Coursework Details
- 8 x online multiple choice question tests (10%) , Centrally-timetabled examination (On-campus) (60%) , Group Project (15%) , Participation (5%) , Test (10%)
- Exam Timing
- Summer