Module code: MAND039

Module Overview

The module provides the analytical tools we need for deriving the limiting distribution of estimators in the context of linear models (OLS and instrumental variables) and nonlinear models (NLS and Generalized Method of Moments). Since in finite sample, asymptotic approximations may be not accurate enough we study how to construct bootstrap critical values, in order to provide more accurate inference.

Module provider

Surrey Business School

Module Leader


Number of Credits: 0

ECTS Credits: 0

Framework: FHEQ Level 8

JACs code: L140

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Statistics tools: Modes of Convergence, Law of Large Numbers, Central Limit Theorems
Consistency and Asymptotic Normality of Ordinary Least Squares Estimators
Hypothesis Testing: Wald, Lagrange Multiplier and Likelihood Ratio Tests
Estimation of Asymptotic Covariance Matrices
Instrumental Variables Estimators: (1) Consistency and Asymptotic Normality, (2) Weak instruments and weak identification
Consistency and Asymptotic Normality of Generalized Method of Moments Estimators (GMM), Tests for Identifying Restrictions
The Bootstrap and its Applications

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework EXERCISES 30
Examination FINAL EXAM PAPER 70

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate

Overall understanding of the material. Ability of connecting the various tools to solve a more general problem.

Thus, in line with domain A’s LO, the summative assessment for this unit consists of:
• Exercises (30%) enabling the students to show and practice their acquired knowledge;
• A final exam paper (70%) enabling the student to show their ability to apply their learning about advanced econometrics.

By-weekly feedback during seminar.

Module aims

  • Given a problem of interest, the economist/econometrician needs to formalize it via a model. Models are approximations to reality and so are typically “wrong”, and this should be taken into account. Once we have collected the data, we use them to estimate our (possibly “wrong”) model, estimate parameters, test hypotheses and make predictions/forecasts. In order to do that in a sensible way, we need to make reasonable assumptions on our data, in terms of how much dependence and/or heterogeneity they display. Given these primitive assumptions, we need to derive the behaviour of our estimators or test statistics as the sample size gets large. Failing to do this correctly, leads us to construct invalid statistics, resulting in unreliable inference.
    The module will provide the necessary analytical tools to become a competent and original user of econometric.

Learning outcomes

Attributes Developed
001 Understand Econometrics papers in top general and top field journals K
002 Formalize the hypotheses of interest K
003 Modify existing tests/estimators to accommodate their own problems K

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

Lecture Hours: 20

Methods of Teaching / Learning

The learning and teaching strategy is designed to:
Try to involve student participation as much as possible.
Seminars will be student driven, with students presenting their solution to the blackboard.

The learning and teaching methods include:

2 hrs of lecture per week, plus one seminar every other week.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list


Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2018/9 academic year.