# EC226: Econometrics 1

• #### Kenichi Nagasawa

Module Lecturer
30 CATS - Department of Economics
Summer Module
Spring Module
Autumn Module

### Principal Aims

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 Learning Outcomes

Acquired the tools of quantitative methods necessary to study core and optional second and third year modules in economics for single honours courses in Economics. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures and classes. The summative assessment methods that measure the achievement of this learning outcome are: Test, exam, and assignment (group work).

Developed their understanding of statistical (econometric) software and economics databases. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures and classes. The summative assessment methods that measure the achievement of this learning outcome are: Tests, assignment (group work).

Further developed their communication skills in presenting and analysing data. The teaching and learning methods that enable students to achieve this learning outcome are: Classes. The summative assessment methods that measure the achievement of this learning outcome are: Tests, assignment (group work).

Developed further their techniques of statistical methods; generated a thorough understanding of the statistical techniques as well as a critical appreciation of them. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures and classes. The summative assessment methods that measure the achievement of this learning outcome are: Test, exam, and assignment (group work).

### 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. Dynamic models with lagged dependent variable. Serial Correlation and implications for OLS. Types of autocorrelation. Nonstationarity and Cointegration. Panel data models. Limited dependent variable models.

### Context

Core Module
L1PA - Year 1, L1P5 - Year 1, LM1D (LLD2) - Year 2, R9L1 - Year 2, R4L1 - Year 2, R2L4 - Year 2, R1L4 - Year 2
Optional Core Module
GL11 - Year 2, GL12 - Year 2, R3L4 - Year 2
Optional Module
GL12 - Year 4, V7ML - 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%) + In-person Examination (60%)
Coursework Details
8 x online multiple choice question tests (10%) , Group Project (15%) , Group work assignment (5%) , In-person Examination (60%) , Test (10%)
Exam Timing
Summer

### Exam Rubric

Time Allowed: 3 Hours, plus 15 minutes reading time.