Functional time series with applications in demography

 

This is a short course given at Humboldt University, Berlin, 24-25 June 2014.

Venue: LvB Library, Room 401, Spandauerstr. 1, 10178 Berlin

Time: 24 June 2014, 09:30 - 12:30 and 14:00 - 17:00 25 June 2014, 09:30 - 11:30

Functional time series are curves that are observed sequentially in time, one curve being observed in each time period. In demography, examples include curves formed by annual death rates as a function of age, or annual fertility rates as a function of age. In finance, functional time series can occur in the form of bond yield curves, for example, with each curve being the yield of a bond as a function of the maturity of a bond.

I will discuss methods for describing, modelling and forecasting such functional time series data. Challenges include:

• developing useful graphical tools (I will illustrate a functional version of the boxplot);
• dealing with outliers (e.g., death rates have outliers in years of wars or epidemics);
• cohort effects (how can we identify and allow for these in the forecasts);
• synergy between groups (e.g, we expect male and female mortality rates to evolve in a similar way in the future, and we expect different types of yield curves to behave similarly over time);
• deriving prediction intervals for forecasts;
• how to combine mortality and fertility forecasts to obtain forecasts of the total population;
• how to use these ideas to simulate the age-structure of future populations and use the results to analyse proposed government policies.

Lectures:

1. Tools for functional time series analysis [Slides]
2. Automatic time series forecasting [Slides]
3. Forecasting functional time series [Slides]
4. Connections, extensions and applications [Slides]
5. Forecasting functional time series via PLS [Slides]
6. Coherent functional forecasting [Slides]
7. Common functional principal components [Slides]
8. Stochastic population forecasting [Slides]