Principles of Econometrics

This information is for the 2016/17 session.

Teacher responsible

Prof Jörn-Steffen Pischke 32L2.16 (MT) and Dr. Marcia Schafgans 32L 4.12 (LT)


This course is compulsory on the BSc in Econometrics and Mathematical Economics. This course is available on the BSc in Accounting and Finance, BSc in Business Mathematics and Statistics, BSc in Economics, BSc in Economics and Economic History, BSc in Economics with Economic History, BSc in Mathematics and Economics, BSc in Mathematics with Economics, BSc in Philosophy and Economics, BSc in Philosophy, Politics and Economics, BSc in Statistics with Finance and MSc in Economics (2 Year Programme). This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.


Students must have completed Mathematical Methods (MA100) and Elementary Statistical Theory (ST102).

Course content

This course is a more advanced introduction to econometrics; it aims to present the theory and practice of empirical research in economics. Compared to EC220, in LT this course puts more emphasis on the underlying statistical theory and relies on the use of matrix algebra.

In MT, the focus of the course is on empirical questions and students will work with the econometrics software package Stata analysing actual data sets. Students will learn how various tools are used to answer causal “what-if” questions (e.g. whether our estimates will deliver answers to questions like: “What is the effect of monetary policy on output?”).

In LT, the focus of the course is on the underlying econometric theory: estimation, properties of estimators (unbiasedness, efficiency, sampling distribution, consistency) and hypothesis testing.

Topics include: randomised experiments; program evaluation; matching; simple and multiple regression analysis; omitted variable bias; functional form; heteroskedasticity and weighted least squares; endogeneity (measurement error, simultaneity); instrumental variables and two-stage least squares; and stationary and non-stationary time series analysis. 

Compared to EC220 this course discusses more advanced topics such as method of moments estimation and maximum likelihood estimation (non-linear) with applications to binary choice and count data models.


30 hours of lectures and 10 hours of classes in the MT. 30 hours of lectures and 10 hours of classes in the LT.

Additional help lectures 10 x 1 hour in the LT.

A one hour revision lecture will be held in week 11 of both the MT and LT.

EC221.B  for graduate students.

Formative coursework

Exercises are provided each week and are discussed in the classes.  (MT) Students are required to hand in written answers to the exercises for feedback. (LT) While students are expected to attempt the weekly problem sets before each class, students will receive formal feedback on 3 occasions.

Indicative reading

J. W. Wooldridge Introductory Econometrics. A Modern Approach, 5th Edition, South-Western.

J. D. Angrist and J. S. Pischke Mastering ‘Metrics. The Path from Cause to Effect, Princeton University Press. 

Further materials will be available on the  Moodle website.

Other useful texts include: W. Greene, Econometric Analysis, 7th Edition, Pearson; J. Johnston and J. Dinardo, Econometric Methods, 4th Edition, McGraw-Hill; G.S. Maddala and K. Lahiri, Introduction to Econometrics, 4th Edition, John Wiley; J.H. Stock and M.W. Watson, Introduction to Econometrics, 3rd Edition, Pearson ; C. Heij et al., Econometric methods with Applications in Business and Economics, Oxford University Press.


Exam (50%, duration: 2 hours) in the LT week 0.
Exam (50%, duration: 2 hours) in the main exam period.

The Lent term examination is based on the Michaelmas term syllabus, and the Summer exam on the Lent term syllabus.

Key facts

Department: Economics

Total students 2015/16: 101

Average class size 2015/16: 11

Capped 2015/16: No

Lecture capture used 2015/16: Yes (MT & LT)

Value: One Unit

Guidelines for interpreting course guide information

PDAM skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Application of numeracy skills