This information is for the 2023/24 session.

Teacher responsible

Dr Vassilis Hajivassiliou SAL 4.23

Dr Ragvir Sabharwal SAL 1.28A

Prof Mark Schankerman SAL 4.30


This course is compulsory on the MRes/PhD in Accounting (EoA) (Economics of Accounting Track) , MSc in Economics and MSc in Economics (2 Year Programme). This course is available on the MPhil/PhD in Environmental Economics and MSc in Economics and Philosophy. This course is available with permission as an outside option to students on other programmes where regulations permit.


Students must have completed Introductory Course in Mathematics and Statistics (EC400).

Students should also have completed an undergraduate degree or equivalent in Economics and an introductory course in Econometrics.

In very exceptional circumstances, students may take this course without EC400 provided they meet the necessary requirements and have received approval from the course conveners (via an online* face to face meeting), the MSc Economics Programme Director and their own Programme Director. Contact the Department of Economics for more information (

Course content

The course aims to present and illustrate the techniques of empirical investigation in economics.  The lectures will focus primarily on the econometric methodology and the required assumptions. This is crucial so that you can assess whether specific techniques are valid in your particular contexts (i.e., whether they will estimate the underlying parameters consistently). Seminars will be a mix of technical exercises and computer-based data applications.


The following material will be covered by Dr Hajivassiliou in the Autumn Term:

• Regression models with fixed regressors (simple and multiple). Least squares and other estimation methods.  Goodness of fit and hypothesis testing.  Estimation Unbiasedness and Consistency.

• Regression models with stochastic regressors.

• Asymptotic theory and its application to the regression model. Sampling error vectors. Large sample approximations.

• The partitioned regression model, multicollinearity, misspecification, omitted and added variables, measurement errors.

• Generalized method of moments.

• Maximum likelihood estimation.

• Heteroskedasticity, autocorrelation, and generalized least squares.  Clustered and Robust Standard Errors.

• Exogeneity, endogeneity, and instrumental variables. The leading causes of endogeneity. Instrument validity and relevance.

• Nonlinear regression modelling

• Binary choice models and other Limited Dependent Variables models.


The following material will be covered by Professor Schankerman in Winter Term (6 weeks):

• Estimating causal effects in panel data: differences in difference estimator, matching methods, and instrumental variables, marginal treatment effects, regression discontinuity.

• Panel data and static models: fixed and random effect estimators, clustering. specification tests.

• Panel data and dynamic models: generalized method of moments.


The following material will be covered by Dr Sabharwal in Winter Term (4 weeks):

• Autoregressive and moving average representations of time series. Stationarity and invertibility.

• Ergodicity, Laws of Large Numbers, and Central Limit Theorems for Time Series

• Vector auto-regressions.

• Unit roots and co-integration.


30 hours of lectures and 10 hours of seminars in the AT. 30 hours of lectures and 9 hours of seminars in the WT. 1 hour of seminars in the ST.

There will be a reading week in Week 6 of WT only (no lectures or classes that week).

This course is delivered through a combination of classes and lectures totalling a minimum 80 hours across Autumn Term, Winter Term and Spring Term.

Formative coursework

Two marked assignments per term. Exercises are provided each week and are discussed in classes. Working through these exercises on a weekly basis is essential for the successful completion of the course. Special test exercises will be set at three points during the year. These will be carefully marked, and the results made available.

Indicative reading

W H Greene, Econometric Analysis (8th edn), traditional presentation of econometric analysis (with emphasis on the material in the Autumn Term)

J Wooldridge, Econometric Analysis of Cross Section and Panel Data (2002): traditional and thorough treatment of panel data in both static and dynamic models (treated in the Winter Term)

J Angrist and J Pischke, Mostly Harmless Econometrics (2009): focuses on modern “causal” methods analysis (treated in the Winter Term)

James D. Hamilton, Time Series Analysis (1994):  traditional presentation of time-series econometric analysis (treated in the Winter Term)


Exam (50%, duration: 2 hours, reading time: 15 minutes) in the January exam period.
Exam (50%, duration: 2 hours, reading time: 15 minutes) in the spring exam period.

Key facts

Department: Economics

Total students 2022/23: 154

Average class size 2022/23: 16

Controlled access 2022/23: Yes

Lecture capture used 2022/23: Yes (MT & LT)

Value: One Unit

Guidelines for interpreting course guide information

Course selection videos

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