BE369-7-SP-CO:
Data Analysis: Cross Sectional, Panel and Qualitative Data Methods

The details
2019/20
Essex Business School
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 13 January 2020
Friday 20 March 2020
20
07 October 2019

 

Requisites for this module
(none)
BE953
(none)
(none)

 

(none)

Key module for

MRESN30012 Finance

Module description

The primary purpose of this module is to provide the student with an understanding of non-time-series data analytic approaches in finance. It covers methods for cross-sectional, panel and qualitative analysis and their applications. All topics are illustrated with relevant examples. The weight given to topics reflects the extent to which methods are used in finance and therefore panel methods are emphasised.
Cross-sectional data are organised over individual groups (eg households, firms or countries) and have no time dimension. They may include discontinuous data (eg binary). Qualitative or categorical data are essentially non-numerical. Examples include survey responses, textual analysis of social media or interviews. Panel data or longitudinal data are multi-dimensional data involving measurements over time. As such, panel data consists of researcher's observations of numerous phenomena that were collected over several time periods for the same group of units or entities. For example, a panel data set may be one that follows a given sample of individuals over time and records observations or information on each individual in the sample. The nature and advantages of panel data has led to numerous applications in finance and economics research.
The course assumes an understanding of core econometrics and time series methods.

Module aims

1The main aims of the course are:
1. to enable students to understand the different methods of data analysis in finance;
2. to enable students to critically evaluate the techniques above and apply them in corporate finance and banking;
3. to understand the nature and scope of financial research and its relevance to finance practice.
4. to understand and discuss critically the relevant literature on financial research.
5. to demonstrate a critical appreciation of various research approaches along with an awareness of both their contribution and limitations.
6. to evaluate different research methods (qualitative and quantitative) and understand their benefits and limitations.

Module learning outcomes

On successful completion of the module, students will be able to:
1. demonstrate a critical understanding of the use of cross-sectional, panel and qualitative data which can be used in areas of finance such as corporate finance and banking and their applications;
2. acquire experience of gathering and analysing qualitative and quantitative data;
3. use a personal computer to code, transform and analyse panel, cross-sectional and survey data;
4. utilise and source information from the library, internet and database sources;
5. undertake predictive estimations and tests of hypotheses using financial data.

Module information

Students must have a basic background in econometrics.

Learning and teaching methods

Learning and Teaching Methods The module is delivered by lectures and classes. Learning takes place through attendance at lectures, preparation for and participation in classes, and private study. Activity Number Frequency Duration Total/Hours Lectures 10 Weekly, from week 25 to 33 2 hours 20 Lab/Classes 5 Once every three weeks 2 hour 10 Preparation & Reading 120 Total 150 The number and format of contact hours, i.e. lectures and classes, will be available on Moodle. A. Lectures (20 hours: 10 lectures of 2 hours each) * The main aims of the lectures are to stimulate your intellectual interest in the subject and to provide you with a structured treatment of the more technical material and a framework to facilitate your private study. * The lectures are used to present material to entire group; lectures may be accompanied with handouts, which will be the focus of the lectures (and obviate the need to take extensive notes in lectures). It is not necessarily the practice of teachers to place detailed lecture notes on Moodle prior to, or even immediately after, the lectures, although some may choose to do so. Attendance at lectures is the expectation, at which any handouts may be distributed. * Although lectures are not designed as interactive, you may have opportunity in lectures to raise issues and confirm their understanding of the analysis where appropriate. * You are expected to attend all lectures and revision sessions. If you do miss a lecture, for some unavoidable reason such as illness, it is your responsibility to catch up on the work missed and also to keep abreast of any announcements, which may have been made at the missed commitment. You are expected to follow lectures by undertaking the reading from the essential readings and other associated guided reading. * Lectures and classes may exceptionally be cancelled, but in that case replacements will be provided. B. Classes/Lab (6 hours: 3 classes/labs of 2 hours each) * Classes are run in a computer lab; * The main aim of the class is to help you, in a structured way, to concretely apply the topic explained during lectures. * These class provide students with different types of data and econometric models that can be used in economics and finance; * Students learn how to estimate the relationship between variables and investigate how reliable the econometric estimates are and how to choose the best estimation technique * Attendance at all classes is compulsory and will be monitored. Physical attendance without having done the work required will be recorded as absence.

Bibliography

  • Saunders, Mark; Lewis, Philip; Thornhill, Adrian. (©2016) Research methods for business students, Harlow: Pearson Education.
  • (no date) Panel Time-Series by Ron P. Smith and Ana-Maria Fuertes.
  • Cameron, A Colin; Trivedi, Pravin K. (2005) Microeconometrics: Methods and Applications: Cambridge University Press.
  • Wooldridge, Jeffrey M. (c2010) Econometric analysis of cross section and panel data, Cambridge, Mass: MIT Press.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework 2,000 words take home assignment 19/02/2020 100%

Overall assessment

Coursework Exam
50% 0%

Reassessment

Coursework Exam
50% 50%
Module supervisor and teaching staff
Theodora Bermpei, Franco Fiordelisi, Shahzad Uddin and Simon Price
ebspgtad@essex.ac.uk

 

Availability
No
No
Yes

External examiner

No external examiner information available for this module.
Resources
Available via Moodle
Of 30 hours, 30 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Essex Business School

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