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2015/16 Taught Postgraduate Module Catalogue

LUBS5289M Panel Data

30 creditsClass Size: 50

Module manager: Kevin Keasey
Email: kk@lubs.leeds.ac.uk

Taught: Semester 2 View Timetable

Year running 2015/16

Pre-requisite qualifications

All students participating in this module must already have obtained the required qualifications to enter the LUBS PhD programme. Students will have at least covered basic statistics and econometrics.

This module is not approved as an Elective

Module summary

The panel data analysis course is run by Professor Julio Pindado of the University of Salamanca. The course is an intensive two week module that aims to enable students to understand and assess the applications of panel data analysis in the business and economics literature. In addition to this the course provides students with the skills necessary to analyze a wide range of research and policy problems utilizing panel data methodologies.

Objectives

The aim of this course is to communicate the skills necessary to understand and assess the applications of panel data analysis reported in the Finance and Business Economics literature, and to provide skills which could be applied to analysing a variety of research and policy problems related to Corporate Finance, Governance and Business Economics.

Learning outcomes
To acquire analytical skills associated with this level of training for postgraduate research so as to enable students to undertake advanced level empirical analysis. On completing the module students will be able to:

- Understand the advantages and limitations of panel data,
- Understand and make informed judgements about the latest approaches towards analysing panel data (including static and dynamic models),
- Understand how to derive economic models from panel data,
- Interpret research findings based on panel data,
- Develop basic skills associated with using panel data and Stata.

Skills outcomes
Participants should understand the basic linear regression model.


Syllabus

The course is intended as an introduction to the issues and opportunities arising when a panel data structure is available. In particular, the course covers the different structures of data and the advantages and limitations of panel data. Additionally, a new approach to modern econometric analysis is provided, highlighting the role of conditional expectations. Both static and dynamic models for panel data analysis are presented, with special attention to choosing the most suitable estimator for each model. As a result, the course focuses on the decisions that the researcher should make instead of the algebraic derivation of the models. Moreover, several cases on how to derive economic models combining panel data and Stata are discussed - this allows students to understand how the outputs of the panel data analysis might be interpreted. Finally, the efficient research process using panel data and Stata is shown by analysing a case.

Teaching methods

Delivery typeNumberLength hoursStudent hours
On-line Learning33.009.00
Group learning33.009.00
Lecture83.0024.00
Practical33.009.00
Private study hours249.00
Total Contact hours51.00
Total hours (100hr per 10 credits)300.00

Private study

Students are expected to read around the topic of panal data by using the suggested reading lists and reference papers. They are expected to come fully prepared to each lecture. They are expected to interact with the tutor, to fully participate in the group work and to then use the skills learnt within their future research projects.

Opportunities for Formative Feedback

An attendance record is kept for each lecture and class. The tutor provides question and answer sessions throughout the course. The tutor also offers one to one feedback sessions.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
Computer Exercise.100.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 05/08/2011

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