This information is for the 2020/21 session.
Dr Vassilis Hajivassiliou 32L.4.23 and Dr Rachael Meager 32L.3.13
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 MPA in International Development, MPA in Public Policy and Management, MPA in Public and Economic Policy, MPA in Public and Social Policy, MPA in Social Impact, MPhil/PhD in Environmental Economics, MSc in Economics and Philosophy and MSc in Quantitative Economic History. 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 (firstname.lastname@example.org).
The course aims to present and illustrate the techniques of empirical investigation in economics.
- Regression models with fixed regressors (simple and multiple). Least squares and other estimation methods. Goodness of fit and hypothesis testing.
- 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.
- Heteroskedasticity, autocorrelation, and generalized least squares.
- Exogeneity, endogeneity, and instrumental variables.
- Nonlinear regression modelling and Limited Dependent Variables models.
- An introduction to Non-classical econometric inference.
- Autoregressive and moving average representations of time series. Stationarity and invertibility.
- Vector auto-regressions.
- Unit roots and co-integration.
- Estimating causal effects in panel data: differences in difference estimator, matching methods, and regression discontinuity.
- Panel data and static models: fixed and random effect estimators, specification tests, measurement errors.
- Panel data and dynamic models: generalized method of moments.
- Binary choice models with heterogeneity.
30 hours of lectures and 10 hours of classes in the MT. 30 hours of lectures and 10 hours of classes in the LT.
This year at least for Michaelmas Term, some or all of this teaching may have to be delivered through a combination of virtual webinars, online videos, and virtual classes.
Two marked assignments per term. Exercises are provided each week and are discussed in classes. In order to have any chance of completing the course successfully, these exercises must be attempted. Special test exercises will be set at three points during the year. These will be carefully marked and the results made available.
W H Greene, Econometric Analysis (6th edn), James D. Hamilton, Time Series Analysis (1994), J Wooldridge, Econometric Analysis of Cross Section and Panel Data (2002), J Angrist and J Pischke, Mostly Harmless Econometrics (2009)
Exam (50%, duration: 2 hours, reading time: 10 minutes) in the January exam period.
Exam (50%, duration: 2 hours, reading time: 10 minutes) in the summer exam period.
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Total students 2019/20: 171
Average class size 2019/20: 19
Controlled access 2019/20: Yes
Value: One Unit