TIME SERIES ECONOMETRICS - 2023/4

Module Overview

By the end of the module students will have learnt how to carry out empirical analyses using appropriate econometric software to study economic and financial time series data; how to interpret the results of such analyses; and will have acquired an ability to critically assess empirical papers.

 

Module provider

Economics

Module Leader

VOLPICELLA Alessio (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

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

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

Indicative content includes:

• The classical linear regression model: review of underlying statistical theory. Properties of estimators and test statistics.


• Stationary Time Series Models.


• Modeling Volatility in Financial Time Series.


• Dynamic models: distributed lags and models of expectations, error correction models.


• Econometric modeling methodology: general to specific modeling strategy for econometric time series models. VAR models.


• Random walks, tests for unit roots, structural breaks. Cointegration.

 

 

Assessment pattern

Alternative Assessment

Not applicable.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of econometric methods that are commonly used in analysing time series data, and the ability to use relevant computer packages to investigate real world economic problems.

Thus, the summative assessment for this module consists of:

• Class test worth 35% of the final mark


• Final exam worth 65% of the final mark

Formative assessment and feedback

Students receive verbal feedback during lectures and tutorials through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). In addition to this, they receive guideline solutions to tutorial questions, against which they can compare their own results. After the class test feedback is provided.

Module aims

• To provide students with the theoretical and practical skills necessary to construct state of the art, single and multi-equation time series econometric models.
• To equip students with the ability to undertake, understand, and critically assess empirical work in economics,
• To promote the students' ability to use econometrics to catalogue and describe empirical regularities and test various propositions.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Give students the theoretical tools they need to go out and analyse real world situations.


• Encourage rigour in their approach to problems.


• Encourage hands-on study of empirical problems.

The learning and teaching methods include:

• 2 hour lecture per week x 11 weeks.


• 1 hour tutorial/lab session per week x 10 weeks.

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: ECO3003

Other information

The School of Economics 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 particularly in the following areas:

Digital capabilities: Students will develop the capacity to manage information and databases pertaining to various types of data. the module also requires students to use software to showcase familiarity of with a wide range of statistical and descriptive techniques. 

Employability: Students are equipped with theoretical and practical problem solving skills, and transferable mathematical and theoretical knowledge that will allow them to analyze in theory and in practice the data driven financial and economic applications. All of this highly valuable to employers for different roles. 

Global and cultural capabilities: Students learn more about real-world examples, working with data from different and heterogeneous countries.

Resourcefulness and Resilience: Key event sessions, normalize feelings of anxiety and stress via class dialogue and discussion, design ‘Service Learning’-based assessment or module activities, design-in small group tutoring, student-led, solution-focused, independent learning approach.

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.