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EC910: Quantitative Methods: Econometrics B

  • Wiji Arulampalam

    Module Leader
  • Eric Renault

    Module Lecturer
50 CATS - Department of Economics

Principal Aims

The module provides students with a thorough understanding of material needed for empirical quantitative analysis, particularly applied econometrics. You will understand how to produce high quality empirical econometric analysis using cross-sectional, time-series, and panel data, and also learn to interpret critically empirical results.

Principal Learning Outcomes

Subject Knowledge and Understanding:...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. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: test, final examination and an empirical project assessment.

Subject Knowledge and Understanding:... demonstrate a deep understanding of material needed for empirical quantitative analysis. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, and independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination and an empirical project assessment.

Subject Specific and Professional Skills:...demonstrate a full knowledge of the theory and practice of modern econometrics, particularly applied econometrics. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, and independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination and an empirical project assessment.

Key Skills:...produce high quality empirical econometric analysis. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, and independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination and an empirical project assessment.

Cognitive Skills:... 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 teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, and independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination and an empirical project assessment.

Syllabus

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:

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

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.

Context

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

Assessment

Assessment Method
Coursework (35%) + 3 hour examination (Summer) (65%)
Coursework Details
Project (25%) - Eligible for self-certification: No, Test1 ( locally held) (4%) - Eligible for self-certification: No, Test 2 (locally held) (6%) - Eligible for self-certification: No, 3 hour examination (Summer) (65%) - Eligible for self-certification: No CATS:
Exam Timing
N/A

Exam Rubric

Time Allowed: 3 Hours plus 15 minutes reading time.

Read all instructions carefully- and read through the entire paper at least once before you start entering your answers.

Sections A and B are based on the material covered in Term 1 and Sections C and D are based on the material covered in Term 2.

There are FOUR Sections in this paper. Answer the ONE question in Section A (30 marks), ONE of TWO questions in Section B (20 marks), the ONE question in Section C (30 marks) and ONE of TWO questions in Section D (20 marks).

You should not submit answers to more than the required number of questions. If you do, we will mark the questions in the order that they appear, up to the required number of questions in each section.

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.

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