ADVANCED ECONOMETRICS 1 - 2023/4

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.

Module provider

Economics

Module Leader

CORRADI Valentina (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 1

Prerequisites / Co-requisites

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

Alternative Assessment

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. 


• A final 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

Methods of Teaching / Learning

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

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.

Seminars, in which 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.

Reading list

https://readinglists.surrey.ac.uk
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

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.