# INTERMEDIATE ECONOMETRICS - 2021/2

Module code: ECO2010

## Module Overview

This module follows on from Introductory Econometrics and considers econometric theory and methods when Gauss Markov assumptions fail to hold. The first half deals with stationary and nonstationary time series. The second half of this module introduces different examples of the endogeneity problem and their solutions.

### Module provider

Economics

GOLSON Eric (Economics)

### Module cap (Maximum number of students): N/A

Independent Learning Hours: 75

Lecture Hours: 22

Tutorial Hours: 10

Guided Learning: 11

Captured Content: 32

Semester 2

## Prerequisites / Co-requisites

ECO2047 (Introductory Econometrics) is a pre-requisite for this module

## Module content

Indicative content includes:

• Introduction to time series methods, Distributed lag models, Autocorrelation

• Lag dependent variable models, Non-stationary time series

• Instrumental variables, two stage least squares

• Simultaneous equations

## Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test ASSESSMENT 1: MID-TERM 20
Examination Online ASSESSMENT 2: EXAM 80

Not applicable

## Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

Their understanding of basic econometric methods, and ability to apply these techniques to analyse time series data and linear models with endogeneity that may arise from omitted variables, unobserved heterogeneity or simultaneous equations.

Thus, the summative assessment for this module consists of:

• Midterm assessment is worth 30% of the final grade.

• Final exam is worth 70% of the final grade.

Formative assessment and feedback

Students receive verbal feedback during lectures and tutorials through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). In addition to this, they receive guideline solutions to tutorial questions, against which they can compare their own results. After the test feedback is provided for all individual questions.

## Module aims

• Introduce students to the techniques relevant for the estimation (i) of econometric time-series models and (ii) in the presence of endogenous variables.
• An important emphasis of the course is to give students with 'hands-on' learning experience of econometric analysis using a variety of economic data sets along side the theory. For this purpose, a number of datasets will be made available to undertake econometric analysis using the EViews software package.

## Learning outcomes

 Attributes Developed 001 Understand a number of concepts relating to OLS estimation with time series data. KCPT 002 Interpret econometric models with a variety of functional forms including those with lagged independent and dependent variables. KCPT 003 Understand various forms of the endogeneity problem and the solutions that can be used to overcome it. This includes methods involving instrumental variables and estimation of simultaneous equations. KCPT 004 Apply econometric techniques using E-views and interpret the output obtained. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Develop skills in analysing economic data in more realistic situations where Gauss Markov assumptions do not hold

• Appreciate the complexities of econometric analysis, understanding importance and intuition behind various estimation strategies and tests

The learning and teaching methods include:

• 2 hour lecture per week x 11 weeks

• 1 hour lab session / tutorials per week x 10 weeks

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.