
Course unit details:
Demographic Forecasting
Unit code | SOST70102 |
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Credit rating | 15 |
Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
Teaching period(s) | Semester 2 |
Available as a free choice unit? | Yes |
Overview
To provide the knowledge and skills required to analyse the population structure and forecast the population change, including the context of the COVID-19 pandemic. Population change is driven by changes in mortality, fertility, and both international and internal migration. The pandemic impacted lives across the globe and made important contributions to the population change due to the highly selective fatality affecting persons at older ages, of male gender, and with comorbidities. Therefore, we derive, interpret and apply a range of demographic measures to the past and current populations with a critical appraisal in the light of the available data sources and their quality. We focus on measures of mortality, such as life tables, multiple decrement and cause-deleted life tables, bilinear and hierarchical models for estimating and forecasting mortality and other components of population change. Methods are applied to the real-world data focusing on outcomes of the COVID-19 pandemic in the UK and other countries, and critically interpreted.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Introduction to Statistical Modelling | SOST70011 | Pre-Requisite | Compulsory |
Statistical Foundations | SOST70151 | Pre-Requisite | Compulsory |
Aims
To provide the knowledge and skills required to analyse the population structure and forecast the population change, including the context of the COVID-19 pandemic. Population change is driven by changes in mortality, fertility, and both international and internal migration. The pandemic impacted lives across the globe and made important contributions to the population change due to the highly selective fatality affecting persons at older ages, of male gender, and with comorbidities. Therefore, we derive, interpret and apply a range of demographic measures to the past and current populations with a critical appraisal in the light of the available data sources and their quality. We focus on measures of mortality, such as life tables, multiple decrement and cause-deleted life tables, bilinear and hierarchical models for estimating and forecasting mortality and other components of population change. Methods are applied to the real-world data focusing on outcomes of the COVID-19 pandemic in the UK and other countries, and critically interpreted.
Learning outcomes
Syllabus
1. Population balance equation – main components of population change
2. Introduction to Bayesian inference
3. Log-linear models and forecasting
4. Modelling age schedules and Lee-Carter model
5. The demographic and epidemiological transitions
6. Introduction to life tables and visualisations
7. Multiple decrement life tables
8. Modelling fertility
9. Presentations of the group work
Teaching and learning methods
8 x Asynchronous lectures + Q&A session (1 hour) + 8 x Tutorial - lab session (2 hours)
Knowledge and understanding
- Understanding of the key concepts and theories related to population change and population components
- Understanding the key measures used to analyse population change
Intellectual skills
- Understanding and critically appraise the methods and data used to measure and forecast population change
Practical skills
- Produce a range of demographic measures using statistical techniques in R software
- Evaluate the quality of the claims by the media and statistical authorities about the population change
Transferable skills and personal qualities
- Apply the learnt methods to the real world data and other settings such as at local authorities, governments and companies that utilise population estimation and forecasting as part of their activities
Assessment methods
[Group work] Critical appraisal of one or two selected journal articles Slides & Talk – worth a maximum of 1000 words (40%)
Analysis of a given aspect of population change/producing population forecasts/assessing impact of population change of a selected country/region accompanied by visualisations, software output and code (in R) – a maximum of 1500 of written essay text; no limit on computer code and output (60%)
Feedback methods
Feedback available via Turnitin.
Recommended reading
Indicative Reading
• Bryant, J., & Zhang, J. L. (2018). Bayesian demographic estimation and forecasting. CRC
Press.
• Preston, S., Heuveline, P., & Guillot, M. (2000). Demography: measuring and modelling population processes.
Additional Materials
• Castles, S., De Haas, H., & Miller, M. J. (2013). The age of migration: International population movements in the modern world. Palgrave Macmillan.
• Rowland, D. T. (2003). Demographic methods and concepts. OUP Catalogue.
• Bijak, J. (2010). Forecasting international migration in Europe: A Bayesian view (Vol. 24). Springer Science & Business Media.
• Gerland, P., Raftery, A. E., &Sevcíková, H., Li, N., Gu, D., Spoorenberg, T., ... & Bay,
G. (2014). World population stabilization unlikely this century. Science, 346(6206), 234-237.
• Wiśniowski, A., Smith, P. W., Bijak, J., Raymer, J., & Forster, J. J. (2015). Bayesian
population forecasting: extending the Lee-Carter method. Demography, 52(3), 1035-1059.
Study hours
Scheduled activity hours | |
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Lectures | 27 |
Independent study hours | |
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Independent study | 123 |
Teaching staff
Staff member | Role |
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Arkadiusz Wisniowski | Unit coordinator |
Additional notes