EC5229 Econometric Time Series Analysis

Academic year

2025 to 2026 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Planned timetable

To be arranged.

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Prof J R McCrorie

Prof J R McCrorie
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Roderick McCrorie

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module provides a treatment of Time Series Analysis as it pertains to Economics and Finance. It provides students with theory and methods tailored to analysing datasets in such settings. The rudiments of univariate and multivariate time series analysis are introduced and then applied to various contexts including some standard macroeconometric models. There is a treatment of non-stationarity, including unit roots and co-integration, and of non-linear models, including threshold models and volatility models used in Finance. Bayesian methods and machine learning are introduced. The module is designed to equip students to use time series methods in their M.Sc. dissertation and to provide foundational knowledge that can be developed in future Ph.D. research. Students are expected to have intermediate-level knowledge of matrix algebra, calculus and statistics.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST TAKE EC5207

Assessment pattern

25% Coursework, 75% exam

Re-assessment

Exam 100%

Learning and teaching methods and delivery

Weekly contact

16 hours of lectures over 10 weeks, 1-hour tutorial (x 5 weeks)

Scheduled learning hours

21

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

129

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Know the elementary properties of econometric time series models such as AR, MA, ARMA, VAR and SVAR models, and of the usual estimators pertaining to the same
  • Use the empirical results studied to see how the models are applied in the areas of macroeconomics and finance
  • Understand the importance of testing for stationarity and non-stationarity and describe tests to implement the same
  • Understand the issues underpinning estimation and inference in high-frequency models, especially in finance, and be able to describe the implementation of the same via the method of maximum likelihood
  • Establish a foundation that is preparatory for research in econometrics, time series analysis, and/or macroeconometrics and finance