Flexible learning
Online postgraduate courses
Time Series Econometrics and Forecasting
The purpose of this core course is to provide students with an in-depth understanding of the fundamental concepts of time series econometrics and forecasting and with the practical skills to use econometric software to model and forecast economic time series and identify models with the best forecasting abilities. The module would build on the foundation of econometrics core course and prepare students for MSc and PhD research.
This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.
- 20 Credits
- 200 hours of study
- 25 contact hours
- 175 hours for private study
Faculty
Structure
Structure
Time Series Econometrics and Forecasting is a core 20 credit course and therefore students are expected to input approximately 200 hours of study into the course.
The total number of contact hours is 25 hours. This leaves 185 hours for private study.
Lectures
This module consists of 4 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day.
There will be optional clinics on the last day of the course.
The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact edu@timberlake.co.uk.
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Module Programme
Introduction and key concepts
- Definitions of time series
- Strictly and weakly stationary processes
Moments, stochastic processes and lag operators
- Conditional moments
- Simple stochastic processes
- Lag operators and data transformations
The autoregressive model of order 1
- Definition of AR(1)
- Stability and moments
ARMA models
- Overview of ARMA models
- Stability and moments
ARMAX models
- Overview of ARMAX/ADL models
- Stability and moments
- Short-run and long-run multipliers
Forecasting and impulse response functions
- Forecasting and IRFs for ARMA models
- Forecasting and IRFs for ARMAX plus ARMA models
- Examples
ARMA model estimation
- The maximum likelihood estimator
- Estimator properties
ARMA model specification
- General-to-specific estimation
- Residual testing
Testing and validation
- Testing and diagnostic checking
- ARMA model validation and comparison
Multivariate Time Series
- Concepts and properties
- VARs and seemingly unrelated regression
Estimation and forecasting of VAR models
- Estimation by ML
- Forecasting vector models
- Impulse response functions
Unit Roots and Stationarity Tests
- Introduction to the notion of stationarity and unit roots
- The Dickey-Fuller unit root test
- Other stationarity and unit root tests
SARIMA Models
- The Box-Jenkins methodology
- Integrated ARMA models
- Seasonal ARIMA models
Prerequisites
English Language Requirements
Both Programmes are open to applicants anywhere in the world. We may ask applicants to provide a recognised English language qualification, dependent upon their nationality and where they have studied/worked previously.
The requirement is an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We will also consider other English language qualifications. If their score is below our requirements, they may be eligible for one of Lancaster University's pre-sessional English language programmes.
Academic Requirements
Applicants to the Postgraduate Certificate of Achievement, Postgraduate Certificate, Postgraduate Diploma or full MSc in either programme require either an upper second-class degree in economics, econometrics or related subjects.
Learning Outcomes
- Understand how to model a univariate or multivariate time series process
- Understand the Maximum Likelihood estimation method
- Distinguish between stationary and nonstationary series and understand the implications of usingnonstationary series
- Understand how to forecast a univariate or a multivariate time series process
- Be able to conduct forecast evaluation with statistical and economic loss function
- Build, estimate and forecast from univariate and multivariate time series models using econometricsoftware
- Understand and critically evaluate recent research in forecasting
- Engage in abstract thinking by extracting the essential features of complex systems to facilitateproblem solving and decision-making
- Communicate and present complex arguments in oral and written form with clarity and succinctness
- Present, interpret and analyse information in numerical form
- Utilise effectively statistical and other packages
- Apply econometrics techniques to aid understanding of the financial and macroeconomic environment
- Work effectively both individually and within a team environment.
Frequently Asked Questions
All sessions are conducted live and online at a scheduled time, but are also recorded. Students may attend live and watch the recordings back to recap the material or watch the recordings only if unable to attend live. We always advise students to attend live where possible as this will allow them the best opportunity to engage with the content and ask the lecturer's questions.
All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.
No, all necessary software is provided to students.
All courses are recorded and available on the LUMS internet platform throughout the current academic year. They can therefore be viewed 24 hours a day.
Timberlake Consultants and Lancaster University Management School (LUMS) Economics department have a longstanding partnership; combining 40+ years of industry expertise with over 50 years of academic excellence. We are delighted to build on this with our micro-credential postgraduate courses.
Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education
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Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education