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EC910: Quantitative Methods: Econometrics B

50 CATS - Department of Economics

Principal Aims

The aim of the module is to give students a good grounding in maths, statistics and modern econometric techniques. Within the econometrics element students will study the ways in which the techniques are applied in the empirical analysis of economic data. This module will supplement the development of these key and fundamental professional skills, by looking at more advanced topics.

Principal Learning Outcomes

By the end of the module the student should be able to: demonstrate an understanding of fundamental concepts in mathematics and statistics relevant to the other core modules and be able to apply these concepts to economics; demonstrate a deep understanding of material needed for empirical quantitative analysis; demonstrate a full knowledge of the theory and practice of modern econometrics, particularly applied econometrics; produce high quality empirical econometric analysis; interpret critically empirical results, including the vast array of diagnostic and test statistics often reported, and to come to a balanced view concerning the weight of the empirical evidence presented.


The syllabus for this module will be based on the following topics; however this list is not limited to those listed below and does not infer all of these topics will be studied in the module:

Pre-sessional Introductory Mathematics and Statistics: topics covered will typically include linear algebra, multivariate calculus and constrained optimisation, differential and difference equations, basic probability theory and hypothesis testing.

Econometrics: The first term will emphasise microeconometric applications, and will cover: properties of estimators and how to generate different estimators (Maximum Likelihood Estimation, least squares, method of moments); discrete choice models (binary, unordered multinomial); censored and truncated dependent variable models (Tobit, endogenous selection - Heckman, switching regression models); Linear panel data models; Treatment evaluation methods.

The second term covers structural econometric modelling (endogeneity and instrumental variables) as well as time series econometrics for macroeconometrics and finance. This will include the investigation of dynamic econometric modelling based on ARMA, GARCH, Vector AutoRegression, Stochastic Volatility and State Space models. Inference techniques include Maximum Likelihood, Generalized Method of Moments and Simulation-based Inference.


Pre or Co-requisites
An undergraduate module in introductory econometrics and basic knowledge of matrix algebra.


Assessment Method
Coursework (35%) + 3 hour exam (65%)
Coursework Details
1 x 1 hour test (4%) and 1 x 2 hour test (6%) on pre-sessional Introductory Mathematics and Statistics + 3000 word project (25%)
Exam Timing

Exam Rubric

Time Allowed: 3 Hours.

Answer THREE questions; AT LEAST ONE question must be from Section A and AT LEAST ONE question must be from Section B. Answer Section A questions in one booklet and Section B questions in a separate booklet.

Approved pocket calculators are allowed.

Read carefully the instructions on the answer book provided and make sure that the particulars required are entered on each answer book. If you answer more questions than are required and do not indicate which answers should be ignored, we will mark the requisite number of answers in the order in which they appear in the answer book(s): answers beyond that number will not be considered.

Previous exam papers can be found in the University’s past papers archive. Please note that previous exam papers may not have operated under the same exam rubric or assessment weightings as those for the current academic year. The content of past papers may also be different.

Reading Lists