EC226: Econometrics 1
Jeremy Smith
Module Leader
Thijs Van Rens
Module Lecturer
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
Subject Specific Skills
- Applied Economics
- Economic information
- Economic principles
- Research and debate
- Abstraction
- Analysis of incentives
- Analytical reasoning
- Analytical thinking and communication
- Creative thinking
- Critical thinking
- Policy evaluation
- Problem solving
Transferable Skills
- Data-based skills
- IT skills
- Numeracy and quantitative skills
- Research skills
- Information technology
- Math, Statistical, data-based research skills
- Oral communication
- Team work skills
- Written communication
- Coding Skills ( Stata, R and Latex)
Exam Rubric
Time Allowed: 3 Hours, plus 15 minutes reading time during which you may make notes on the question paper. You must not start writing in your answer booklet until you are instructed.
Read all instructions carefully - and read through the entire paper at least once before you start entering your answers.
A formula sheet and statistical tables are provided.
There is ONE section in this paper. Answer ALL questions (10 marks each).
Use separate booklets as instructed below.
• Use a GREEN booklet for Q1 and Q2.
• Use a SEPARATE GREEN booklet for Q3 and Q4.
• Use a SEPARATE GREEN booklet for Q5 and Q6.
• Use a SEPARATE GREEN booklet for Q7 and Q8.
• Use a SEPARATE GREEN booklet for Q9 and Q10.
You must write the number(s) of the question(s) you have answered on the front cover of each booklet. Make sure the numbers are clearly visible and correspond to the questions you completed inside that booklet.
Approved scientific (non-graphical) pocket calculators are allowed. Statistical tables and a formula sheet are provided.
Previous exam papers can be found in the University’s past papers archive. Please note that previous exam papers may not have operated under the same exam rubric or assessment weightings as those for the current academic year. The content of past papers may also be different.