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BAEcon Economics and Finance / Course details

Year of entry: 2021

Course unit details:
Quantitative Methods

Unit code ECON20222
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 2
Offered by Economics
Available as a free choice unit? Yes

Overview

The aims of this unit are to:

The general purpose of this course is to provide students with a non-technical introduction to the basic methods of econometrics. The focus will be on:

1.     enabling students to perform basic data handling (uploading, re-categorising, cleaning) in a statistical software and to implement the statistical techniques taught in the course in that software;

2.     giving students a basic understanding and working knowledge of multivariate regression;

3.     developing students’ understanding of the potential outcomes framework and its implications for drawing causal inference;

4.     developing students’ understanding of popular techniques of establishing causal relationships;

5.     understanding issues arising from non-stationary time-series and gain a beginning understanding of time-series modelling and forecasting

linking the teaching of techniques to real-life problems

Knowledge and Understanding:

At the end of this course students should be able to:

•       Understand how the use of statistics and econometrics can inform substantive discussion

•       Have obtained a firm understanding of summary statistics

•       Understand the basic tenants of regression analysis

•       Understand issues arising from non-stationary time-series

•       Understand the difficulties in establishing causal relationships

•       Understand popular techniques of establishing causal relationships

•       Be able to handle complex dataset and perform basic data-cleaning tasks

Be able to perform statistical analysis in a software package (R)

Pre/co-requisites

Unit title Unit code Requirement type Description
Adv Maths - BAEcon & BSc Econ ECON10071 Pre-Requisite Compulsory
Advanced Statistics ECON10072 Pre-Requisite Compulsory
Introductory Mathematics ECON10061 Pre-Requisite Compulsory
Introductory Statistics for Economists SOST10062 Pre-Requisite Compulsory
ECON20222 Prerequisites: (ECON10071 and ECON10072) or ECON10061 and SOST10062)

(ECON10071 and ECON10072) or (ECON10061 and SOST10062)

Aims

The aims of this unit are to:

The general purpose of this course is to provide students with a non-technical introduction to the basic methods of econometrics. The focus will be on:

1.     enabling students to perform basic data handling (uploading, re-categorising, cleaning) in a statistical software and to implement the statistical techniques taught in the course in that software;

2.     giving students a basic understanding and working knowledge of multivariate regression;

3.     developing students’ understanding of the potential outcomes framework and its implications for drawing causal inference;

4.     developing students’ understanding of popular techniques of establishing causal relationships;

5.     understanding issues arising from non-stationary time-series and gain a beginning understanding of time-series modelling and forecasting

linking the teaching of techniques to real-life problems

Learning outcomes

Knowledge and Understanding:

At the end of this course students should be able to:

  • Understand how the use of statistics and econometrics can inform substantive discussion
  • Have obtained a firm understanding of summary statistics
  • Understand the basic tenants of regression analysis
  • Understand issues arising from non-stationary time-series
  • Understand the difficulties in establishing causal relationships
  • Understand popular techniques of establishing causal relationships
  • Be able to handle complex dataset and perform basic data-cleaning tasks

Be able to perform statistical analysis in a software package (R)

Syllabus

The exact syllabus may vary and the following is indicative

Week 1 – An Introduction to R and RStudio and Summary Statistics I
Understanding the basic workings of R. Importing data. Basic data operations including summary statistics. How to fix problems.

Week 2 – An Introduction to R and RStudio and Summary Statistics II
Slicing data in different dimensions and conditional summary statistics.

Week 3 – Multivariate Regression Analysis – and Applications in R
Assumptions and resulting properties of multivariate regression analysis. Omitted variable bias.

Week 4 – Inference – and Applications in R
Basic inference problem. Test statistics and their distribution under the null hypothesis. P-values. Assumptions and robust inference (as standard!). t-tests and F-tests.

Week 5 – Causal relationshipsSelection issues and applications of regression models
An introduction into the potential outcomes framework. Introduce selection problems (on observables and un-observables). Understanding that regression helps when selection is on observables. Mention of matching in case the linear functional form is restrictive (no application here). Selection on unobservable as the source of difficulties.

Week 6 – Randomised Control Trials – and Applications in R
Students to help collecting data with a RCT (e.g. question on tax fairness with different priming information).

Week 7 – Panel data and Difference in Difference – and Applications in R
Cross-Section or Panel structure of data. How to use Panel data to remove time-invariant unobservables.

Week 8 – Regression discontinuity – and Applications in R (this could be dropped)

Week 9 – Correlation, Causation and non-Stationarity
Understanding the characteristics of non-stationary data. Spurious regressions.

Week 10 – Economic forecasting – Understanding the pitfalls
Use Surveys of Professional forecasters to understand variation in forecasts and uncertainty embodied in forecasts. Use AR(1) model to produce basic forecasts. Basic tenants of forecast evaluation. Evaluating forecasts for binary outcomes.

Week 11 – End of term test

Intellectual skills

(i) problem-solving skills; (ii) ability to analyse and interpret empirical data; (iii) the evaluation and critical analysis of arguments, theories and policies; (iv) synthesise and evaluate data.  

Practical skills

(i) ability to analyse and interpret empirical data; (ii) basic working knowledge of statistical software.

Transferable skills and personal qualities

(i) select and deploy relevant information; (ii) communicate ideas and arguments in writing and verbally; (iii) apply skills of analysis and interpretation; (iv) manage time and work to deadlines; (v) use ICT to locate, analyse, organise and communicate information (e.g. internet, on-line databases, search engines, library catalogues, spreadsheets, specialist programs, word processing and presentation software) (vi) ability to work in a small group.

Assessment methods

  • Online test (on the use of R) 10%
  • End-of-Term in class MC and short answer questions 50%
  • Group coursework 40%

Feedback methods

Online quizzes, Practice questions in computer and exercise classes, office hours, discussion board

Recommended reading

Joshua D. Angrist & Jörn-Steffen Pischke (2014) Mastering 'Metrics: The Path from Cause to Effect, Princeton University Press

Study hours

Independent study hours
Independent study 0

Teaching staff

Staff member Role
Martyn Andrews Unit coordinator
Ralf Becker Unit coordinator

Additional notes

For every 10 course unit credits we expect students to work for around 100 hours. This time generally includes any contact times (online or face to face, recorded and live), but also independent study, work for coursework, and group work. This amount is only a guidance and individual study time will vary.

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