EC5221 Econometric Time Series Analysis

Academic year

2024 to 2025 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

20

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

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 and Nicky Grant

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 will provide a thorough advanced treatment of the core theory and practice of time series econometrics. It examines the models and statistical techniques used to study time series data in economics. The first objective is to lay out the econometric theory of time series analysis and the second is to equip students who will use time series data or methods in their future Ph.D. research with some of the tools they will need. 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 EC5203

Assessment pattern

3-hour Written Examination = 75%, Coursework = 25%

Re-assessment

3-hour Written Examination = 100%

Learning and teaching methods and delivery

Weekly contact

2 lectures (X10 weeks), occasional tutorials

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