Module Provider: APD
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Autumn / Spring term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Prof Kelvin Balcombe

Email: k.g.balcombe@reading.ac.uk

Type of module:

Summary module description:

Learn how to evaluate different econometric models using different types of data to answer questions in economics and other social sciences, through a combination of lectures and practical classes. Work with econometric models that can deal with different types of dependent variables (continuous, categorical, censored), and different types of data (cross-section and time-series). Carry out different types of hypothesis testing and learn how to interpret the results. Learn how to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from microeconomics to finance and marketing. The prerequisites for this course are familiarity with elementary mathematics and statistics.


This module provides an introduction to different econometric models as applied to different types of data. At the end of this module students should be able to

  • translate different types of data into appropriate econometric models to make forecasts and to support decision making

  • specify model such that they can be used to answer a specific research questions

  • conduct hypothesis testing and interpret results critically

  • be aware of and familiar with various type of shortcoming in data and how to address them

  • handle data sets and use the software Gretl to carry out econometric analysis of different types of data using different types of models

Assessable learning outcomes:

At the end of the modules, students should be able to:

  • Understand how to specify and estimate various econometric models applied to different types of data (cross-section, time series)

  • Interpret and critically evaluate results obtained from a variety econometric models applied to different types of data

  • Be aware of common problems associated with different types of data and methods of addressing them

  • Combine data handling skills and econometric software skills to undertake applied econometric analysis and evaluate and interpret results

Additional outcomes:

Outline content:

Autumn Term

  1. Probability Theory I

  2. Probability Theory II

  3. Simple regression Models

  4. Multiple Regression Models I

  5. Multiple Regression – Application

  6. Multiple Regression Models II

  7. Single & joint restrictions

  8. Hypothesis Testing – p-values

  9. Logistic regression

  10. Logistic Regression – Application

  11. Spring Term

    1. Coursework assignment

    2. Model specification I

    3. Model specification II

    4. Model specification – Application

    5. Endogeneity & Instrumental Variables

    6. Heteroscedasticity & Autocorrelation

    7. Time Series

    8. Time Series – Application

    9. Ordered logistic regression + Tobit model

    10. Ordered logistic regression + Tobit model – Application

Brief description of teaching and learning methods:

Lectures will provide an understanding of fundamental concepts and demonstrate the use of data analysis methods. Practical classes will involve students analysing real data sets with a focus on learning the concepts taught in the lectures.

Contact hours:
  Autumn Spring Summer
Lectures 20
Tutorials 10
Practicals classes and workshops 20
Guided independent study: 150
Total hours by term 200
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Written assignment including essay 80
Class test administered by School 20

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:


Number and length of assignments and in-class tests, and submission date for each assignment (expressed as a week of a specific Term):

• Two In-class tests – 1x autumn 1x spring

• Report (2,500 words) – autumn term

• Report (2,500 words) – spring term

Formative assessment methods:

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy “Penalties for late submission for Postgraduate Flexible programmes”, which can be found here: http://www.reading.ac.uk/web/files/qualitysupport/penaltiesforlatesubmissionPGflexible.pdf
The Support Centres will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
A mark of 50% overall.

Reassessment arrangements:
Coursework assignment to be carried out in August.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 3 November 2020


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