Probabilistic Population Projections: Theory and Practice
Start: 24 November 2014
End: 28 November 2014
Location: MPI Rostock
Hana Sevcikova (University of Washington)
Adrian Raftery (University of Washington)
Population projections are usually done deterministically using the cohort component method, yielding a single value for each projected future population quantity of interest. Recently, the United Nation Population Division adopted a probabilistic approach to project fertility, mortality and population for all countries. In this approach, the total fertility rate and female and male life expectancy at birth are projected using Bayesian hierarchical models estimated via Markov Chain Monte Carlo. They are then combined with a cohort component model which yields probabilistic projection for any quantity of interest. The methodology is implemented in a suite of R packages which has been used by the UN analysts to produce the most recent revision of the World Population Prospects.
This course will teach the theory and practice behind the UN probabilistic projections. Ideas of the Bayesian hierarchical modeling for the two main components, the fertility and mortality, will be explained, including a model for female-male gap in life expectancy. In hands-on exercises, students will become familiar with the functionality of the R packages. By the end of the course, they will be able to generate projections using their own data, impute missing values, aggregate, extract probabilistic projections from many quantities of interest using various output formats, e.g. graphs, tables, maps, pyramids etc.
We will alternate between lectures and computer labs. The afternoon's computer labs give students the opportunity to put the theory from the morning's lectures into practice.
Monday, November 24th
1. Introduction to probabilistic population projection (Adrian)
2. Probabilistic projection of the total fertility rate (Adrian)
3. Overview of the R packages to be used (Hana)
Computer lab: Estimation and projection of TFR using the bayesTFR package (Hana)
Tuesday, November 25th
1. Probabilistic projection of life expectancy at birth (Adrian)
2. Model for female-male gap in the life expectancy at birth (Adrian)
Computer lab: Estimation and projection of the life expectancy using bayesLife (Hana)
Wednesday, November 26th
Lecture: Probabilistic population projection, including age specific mortality rates (Hana)
Computer lab: Generating probabilistic population projections using bayesPop (Hana)
Thursday, November 27th
Lecture: Expression language of bayesPop (Hana)
Computer lab: Deriving probabilistic projections of various quantities of interests using bayesPop's expression language (Hana)
Friday, November 28th
Lectures: Special topics: 1. Probabilistic migration projection; 2. Bayesian population reconstruction (Adrian)
Computer lab: Exploring UN projections using wppExplorer (Hana)
Students are expected to have a basic familiarity with R.
Students will be evaluated based on satisfactory solution to all assignments and active participation in class discussions.
A reading list will be provided.
There is no tuition fee for this course. Students are expected to pay their own transportation and living costs. However, a limited number of scholarships are available on a competitive basis for outstanding candidates and for those applicants who might otherwise not be able to come.
Recruitment of students
Applicants should either be enrolled in a PhD program (those well on their way to completion will be favored) or have received their PhD.
A maximum of 20 students will be admitted.
The selection will be made by the MPIDR based on the applicants’ scientific qualifications.
How to apply
Applications should be sent by email to the MPIDR. Please begin your email message with a statement saying that you apply for course IDEM 115 – Probabilistic Population Projections. You also need to include the following three documents, either in the text of the email or as attached documents. (1) A two-page curriculum vitae, including a list of your scholarly publications. (2) A one-page letter from your supervisor at your home institution supporting your application. (3) A two-page statement of your research and how it relates to the course. Please include a short description of your knowledge of R. Please indicate (a) whether you would like to be considered for financial support and (b) if you would be able to come without financial aid from our side.
Send your email to Heiner Maier (firstname.lastname@example.org).
Application deadline is 24 September 2014.
Applicants will be informed of their acceptance by 10 October 2014.
Applications submitted after the deadline will be considered only if space is available.