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20 Credits

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.

Time Series Econometrics and Forecasting
  • 20 Credits
  • 200 hours of study
  • 25 contact hours
  • 175 hours for private study
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Qualifications accredited by Lancaster University
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Buildable Qualifications
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Learn Around
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World-Class
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Fully Online

Structure

Software

EViews 14

Module Programme

Introduction and key concepts

Session Content
  • Definitions of time series
  • Strictly and weakly stationary processes

Moments, stochastic processes and lag operators

Session Content
  • Conditional moments
  • Simple stochastic processes
  • Lag operators and data transformations

The autoregressive model of order 1

Session Content
  • Definition of AR(1)
  • Stability and moments

ARMA models

Session Content
  • Overview of ARMA models
  • Stability and moments

ARMAX models

Session Content
  • Overview of ARMAX/ADL models
  • Stability and moments
  • Short-run and long-run multipliers

Forecasting and impulse response functions

Session Content
  • Forecasting and IRFs for ARMA models
  • Forecasting and IRFs for ARMAX plus ARMA models
  • Examples

ARMA model estimation

Session Content
  • The maximum likelihood estimator
  • Estimator properties

ARMA model specification

Session Content
  • General-to-specific estimation
  • Residual testing

Testing and validation

Session Content
  • Testing and diagnostic checking
  • ARMA model validation and comparison

Multivariate Time Series

Session Content
  • Concepts and properties
  • VARs and seemingly unrelated regression

Estimation and forecasting of VAR models

Session Content
  • Estimation by ML
  • Forecasting vector models
  • Impulse response functions

Unit Roots and Stationarity Tests

Session Content
  • Introduction to the notion of stationarity and unit roots
  • The Dickey-Fuller unit root test
  • Other stationarity and unit root tests

SARIMA Models

Session Content
  • 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

Key Skills
  • 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
Desired Skills
  • 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

Are the courses within either programme conducted synchronously or asynchronously?

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.

Is all examination undertaken online or in-person?

All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.

Do I need to buy any statistical/econometric software?

No, all necessary software is provided to students.

What do I do if I can't attend a course live?

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.

A Collaboration Like No Other

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.

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