ADVANCED ECONOMETRICS 2 - 2023/4

Module Overview

The module builds up over the material covered in Advanced Econometrics 1, and provides an overview of advanced econometric methods that are essential for empirical analysis. Central topics are quantile regression, panel data models, including the most recent developments such as nonlinear panel models and endogenous attrition. The module also introduces students to the statistical tools used in Microeconometrics, such as Binary Choice Models, in the standard case and in the presence of endogeneity, and limited dependent variables, with special focus on Tobit models and sample selection.

Module provider

Economics

Module Leader

MARTELLOSIO Federico (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 2

Prerequisites / Co-requisites

N/A

Module content

Indicative content includes:

• Nonparametric Estimators

• Density Estimation: Bias and Variance

• Consistency of Conditional Mean Estimators

• Asymptotic Normality and Rates of Convergence

• Issues in Implementing Nonparametric Regression

• Discrete Choice Models

• Probit and Logit

• Endogeneity

• Limited Dependent Variables (including corner solutions)

• Quantile Regression

• Tobit Models

• Sample Selection

• Treatment Effect

• Panel Data Models

• Nonlinear Panel Data Models

• Unbalanced Panel Data Models

• Missing Not at Random

Assessment pattern

Alternative Assessment

None.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their technical skills relating to the use of econometrics techniques to do innovative empirical work.

Thus, the summative assessment for this module consists of:

A three-hour examination worth 100% of the final mark.

Formative assessment

Discussions during and outside lectures. Student will receive verbal feedback during the lectures and workshops through direct interaction, as well through the solution of pre-assigned problems. Office hours are devoted to more targeted, individually based feedback on specific problems.

Module aims

• • provide the advanced tools required to become competent and creative users of econometrics.
• • enable students to combine existing tools so as to find novel ways of solving econometrics problems.
• • enable students to undertake independent research in econometrics

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to ensure student that students achieve the module’s learning outcomes. The weekly lectures will cover in deep the module content. Students are encouraged to actively participate in the lectures. The weekly workshop will focus on practical applications and problem solving with a high interactive content.

The learning and teaching methods include: the study of papers in scientific journals and working through problem sets, to ensure concepts and methods are mastered.

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: ECOM066

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 judgment, 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 specialized statistical software.

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.