# ADVANCED ECONOMETRICS 1 - 2023/4

Module code: ECOM061

## Module Overview

The module provides a rigorous treatment of the linear model and tests for linear restrictions, for the case of independent, identical distributed (iid) observation. We then study nonlinear models and tests for nonlinear restrictions, using the Generalized Method of Moment (GMM) estimator as a leading example. Bootstrap counterpart of estimators and tests will be considered. We finally provide an extension to the case of time series data.  More precisely, 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.

Economics

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

Independent Learning Hours: 67

Lecture Hours: 22

Seminar Hours: 11

Guided Learning: 50

Semester 1

None.

## Module content

Indicative content includes:

• 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

• The Case of Dependent Observations

## Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK (TAKE HOME) 30
Examination FINAL EXAM 70

None

## Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module’s learning outcomes and, by association, developed their digital capabilities,  resourcefulness, and employability, among other module attributes.

Thus, the summative assessment for this module consists of:

• A take-home coursework, which consists of a theoretical part based on problems to solve and a computational part, based on Monte Carlo simulation exercise. Students are allowed to work together on the computational part. The take home coursework is worth 30% of final mark.

• A final exam which is worth 70%.

Formative Assessment

Biweekly feedback during seminar. Intermediate feedback through extended comments to Take Home exam.

## Module aims

• Provide students with an advanced understanding of key statistical and econometric tools
• Enable student to state a hypothesis of interest and derive a test
• Enable students to be competent and innovative econometrics users

## Learning outcomes

 Attributes Developed 001 To understand Econometrics papers in top general and top field journals. KC 002 To formalize the hypotheses of interest. KC 003 To complete a good quality empirical project. KCP 004 To master computing ability in Matlab and/or R KPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

The learning and teaching strategy is designed to ensure that students achieve the module’s learning outcomes.

The two hours weekly lectures will provide a deep insight into the module content. Students are encouraged to actively participate in discussion. The MRes is a small group programme and active interaction stimulates peer effects.

In the weekly hour seminar, the solution of preassigned problems sets will be discussed.

Students will present their solution at the blackboard. This will boost presentation capability as well as team cooperation.

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.

Upon accessing the reading list, please search for the module using the module code: ECOM061

## Other information

Surrey's Curriculum Framework is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience.

This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Employability: Through the module’s learning and assessment activities, students develop independent judgement, collaborative skills as part of a small group with a common goal, and cognitive skills that enable them to reflect critically on their own practice. They also sharpen their ability to analyze and synthesize ideas and methods.

Digital Capabilities: Students will be implementing some of the tools taught in class using program like R and Matlab.

## Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics MRes 1 Compulsory A weighted aggregate mark of 50% is required to pass the module

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 2023/4 academic year.