2020/21 Taught Postgraduate Module Catalogue
LUBS5956M Panel Data
15 creditsClass Size: 20
Module manager: Kevin Keasey
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2020/21
Pre-requisite qualificationsAll 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 summaryThe 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.
ObjectivesThe 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.
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.
Participants should understand the basic linear regression model.
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.
Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information
|Delivery type||Number||Length hours||Student hours|
|Private study hours||278.00|
|Total Contact hours||22.00|
|Total hours (100hr per 10 credits)||300.00|
Private studyThis could include a variety of activities, such as reading, watching videos, question practice and exam preparation.
Opportunities for Formative FeedbackYour teaching methods could include a variety of delivery models, such as face-to-face teaching, live webinars, discussion boards and other interactive activities. There will be opportunities for formative feedback throughout the module.
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Total percentage (Assessment Coursework)||100.00|
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading listThe reading list is available from the Library website
Last updated: 10/08/2020 08:41:53
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