Population Projection and Forecasting
Date:
07/04/2027 - 09/04/2027
Organised by:
NCRM and Department of Social Statistics and Demography, University of Southampton
Presenter:
Dr Jason Hilton is an Associate Professor in Social Statistics and Data Science in the Department of Social Statistics and Demographic, University of Southampton. His research focusses on estimating and forecasting the components of demographic change. He has published articles in a range of demographic and statistical journals and has worked with organisations such as the United Nations Refugee Agency (UNHCR), the Office for National Statistics (ONS), the Greater London Authority and the European Asylum Support Office (now the European Union Agency for Asylum) on population forecasting and estimation projects and problems. He is currently a member of the Office for National Statistics (ONS) National Population Projections Migration Expert Advisory Panel, and an Associate editor for the Journal of the Royal Statistical Society (Series A).
Level:
Intermediate (some prior knowledge)
Contact:
Jacqui Thorp
Training and Capacity Building Coordinator, National Centre for Research Methods, University of Southampton
Email: jmh6@soton.ac.uk or info@ncrm.ac.uk
Location:
View in Google Maps (SO17 1BJ)
Venue:
Room 2015, Building 39
University of Southampton
Highfield
Southampton
Hants
At any time horizon, policy depends upon estimates of population size and structure. Without population forecasts, planning for pension spending, housing demand, healthcare provision, and school place requirements becomes impossible. To know what the size and structure of our population will look like in the future, we need to have the tools to make demographic projections confidently.
This course introduces the methods for forecasting the key components of population change, dealing in turn with mortality, fertility and migration. The course also includes an introduction to matrix models for multi-population forecasting is provided, together with a discussion of how to validate and assess uncertainty in demographic projections.
The course will include hands-on computer workshops using the R programming language.
This course is intended to follow on from a related NCRM course Introduction to Demographic Methods, which will run on 5-6 April 2027. Participants may choose to attend one or both courses as their prior experience and time allows.
The course covers:
The cohort component projection model
Matrix population models
Principal component methods for population estimation and forecasting
Validating and assessing uncertainty in population forecasts
By the end of the course participants will:
- Appreciate contemporary issues in demographic forecasting
- Be able to produce projections of each of the demographic components (mortality, fertility, and migration)
- Produce full population projections using the cohort component model
- Use matrix models for multi-population projection
Pre-requisites
This practical course will include workshops that use the R programming language to demonstrate the techniques introduced in the course. Basic knowledge of R would be beneficial, but is not essential. You will be able to follow the course just by attending the lecture sessions and carefully reading and digesting the workshop material, although of course, you will get more out of the course by completing the workshop exercises in R. Pre-course material will also provide links to free online introductory R courses to aid preparation.
Knowledge of basic demographic techniques is recommended, including:
- The distinction between rates and probabilities in a demographic context
- The life table
- Basic fertility measures
Completion of the companion course Introduction to Demographic Methods would be advantageous but is not required.
Target Audience
- Analysts working at or with National Statistical Offices, International Non-Governmental Organisations, and Government Departments with an interest in population estimation and projection. Examples include the ONS, the DHCLG; local and devolved government bodies; UN agencies.
- PhD students working on population-related topics including demography, population health, human geography, etc.
- Employees of private sector organisations who have an interest in population change:
Consumer-focussed industries;
Retailers
Market research agencies
Utility companies
Construction companies
THIS COURSE IS BEING DELIVERED IN-PERSON AT THE UNIVERSITY OF SOUTHAMPTON (BUILDING 39, ROOM 2015) AND WILL RUN FROM 10:00 to 17:00 ON 5 AND 6 APRIL. IT EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES.
Cost:
The fee per teaching day is:
• £80 per day for students registered at any University.
• £195 per day for staff at academic institutions, recognised research institutions, Research Council-funded researchers, public sector staff and staff at registered charity organisations.
• £245 per day for all other participants
All fees include course materials (in digital format) and refreshments. Fees do not include lunch, travel or accommodation costs.
In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. NO refunds are available after this date.
If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs.
The University of Southampton’s Online Store T&Cs also continue to apply.
Website and registration:
Region:
South East
Keywords:
Quantitative Data Handling and Data Analysis, Demographic Methods, Population Projection, Matrix Population Models, Forecasting
Related publications and presentations from our eprints archive:
Quantitative Data Handling and Data Analysis