# INTRODUCTORY ECONOMETRICS - 2024/5

Module code: ECO2047

## Module Overview

This module provides students with the basic mechanics, both in terms of the theoretical background and the practical skills, for carrying out applied econometrics. Throughout there is an emphasis on understanding the assumptions of the methods so as to gain an appreciation of what such techniques can and cannot deliver. An important part of the course is the training in the use of the econometrics software package Stata. The module relies on the mathematics and statistics students have studied at Level 4, and lays the foundations for subsequent modules in econometrics at Level 5 and 6.

### Module provider

Economics

MARTELLOSIO Federico (Economics)

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

Independent Learning Hours: 51

Lecture Hours: 22

Tutorial Hours: 11

Guided Learning: 44

Captured Content: 22

Semester 1

None

## Module content

Indicative content includes:

• The relationship between economic and econometric models

• Simple OLS regressions

• Multiple OLS regression

• Properties of OLS regression

• Dummy variables

• Model specification

• Heteroskedasticity

## Assessment pattern

Assessment type Unit of assessment Weighting
Online Scheduled Summative Class Test Online Test 1 within a 4hr window 15
Online Scheduled Summative Class Test Online Test 2 within a 4hr window 15
Examination FINAL EXAM (120 MIN) 70

Not applicable

## Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module’s learning outcomes. Thus, the summative assessment for this module consists of:

• One multiple-choice test, connected to learning outcomes 1, 2, and 4 (15%)

• One computer lab test, specifically connected to learning outcome 3 (15%)

• A final exam, connected to learning outcomes 1, 2, and 4 (70%)

Formative assessment and feedback

Students receive verbal feedback during in-person workshops and tutorials. Students are also provided with weekly problem sets which they can solve independently or in teams in preparation for the tutorials. During the tutorials, students receive feedback on their work, and guidance on how their answers could be improved. In addition to this, students receive solutions to the problem sets in the form of a STATA executable file. Weekly learning activities provide the students with feedback on their understanding of the material. Before each of the two tests, sample tests  are made available, along with correct answers and discussion of possible mistakes. After the tests, students receive verbal and written feedback in the form of correct answers and a discussion of common mistakes.

## Module aims

• Introduce students to basic econometric techniques used for the analysis of economic data
• Provide students with the tools necessary for the correct interpretation of empirical work in economics
• Train students to analyze data using use the econometrics software package, Stata

## Learning outcomes

 Attributes Developed 001 Students will be able to turn an economic model into an econometric model KC 002 Students will be able to formulate economic hypotheses as statistical tests and be able to carry out these tests and interpret their results KC 003 Students will be able to show how regression analysis can be implemented using econometric software, and have gained experience in the analysis and use of empirical data in economics KCP 004 Students will be able to provide an economic interpretation of the results of regression analysis KCT

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 ensure that students achieve the module’s learning outcomes. Students have weekly in person lectures and in-person computer lab tutorials. Lectures will review the weekly learning topics and consolidate the weekly learning outcomes by solving exercises. Tutorials are dedicated to data analysis using the econometric software STATA. There is also weekly guided learning, in the form of self-tests with built-in feedback, computer exercises, and other learning activities.

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.

Upon accessing the reading list, please search for the module using the module code: ECO2047

## Other information

The School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities particularly in the following areas:

Digital Capabilities

Students will be trained in the use of an econometric softare package; see learning outcome 3, assessed via the computer lab test

Employability

The data skills developed in this module will enhance students’ employability.

## Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics and Mathematics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Economics and Finance BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Economics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Business Economics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.