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Han Lin Shang and Rob J Hyndman

Journal of Computational and Graphical Statistics (2016) to appear.

Abstract
Age-specific mortality rates are often disaggregated by different attributes, such as sex, state and ethnicity. Forecasting age-specific mortality rates at the national and sub-national levels plays an important role in developing social policy. However, independent forecasts of age-specific mortality rates at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider the problem of reconciling age-specific mortality rate forecasts from the viewpoint of grouped univariate time series forecasting methods (Hyndman et al, 2011), and extend these methods to functional time series forecasting, where age is considered as a continuum. The grouped functional time series methods are used to produce point forecasts of mortality rates that are aggregated appropriately across different disaggregation factors. For evaluating forecast uncertainty, we propose a bootstrap method for reconciling interval forecasts. Using the regional age-specific mortality rates in Japan, obtained from the Japanese Mortality Database, we investigate the one- to ten-step-ahead point and interval forecast accuracies between the independent and grouped functional time series forecasting methods. The proposed methods are shown to be useful for reconciling forecasts of age-specific mortality rates at the national and sub-national levels, and they also enjoy improved forecast accuracy averaged over different disaggregation factors.

Working paper

Online paper

  Tag: functional data

107 posts
September 14th, 2016

Grouped functional time series forecasting: an application to age-specific mortality rates

Han Lin Shang and Rob J Hyndman Journal of Computational and Graphical Statistics (2016) to appear. Abstract Age-specific mortality rates […]

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

Thomas Url1, Rob J Hyndman2, Alexander Dokumentov2 Vienna University of Economics and Business, Vienna, Austria Monash Business School, Monash University, […]

May 26th, 2015

Visualization of big time series data

Talk given to a joint meeting of the Statistical Society of Australia (Victorian branch) and the Melbourne Data Science Meetup Group.

June 24th, 2014

Functional time series with applications in demography

A short course given at Humboldt University, Berlin, 24-25 June 2014.

June 5th, 2014

Low-dimensional decomposition, smoothing and forecasting of sparse functional data

By Alexander Dokumentov and Rob J Hyndman

May 24th, 2014

Common functional principal component models for mortality forecasting

By Rob J Hyndman and Farah Yasmeen
Contributions in infinite-dimensional statistics and related topics. Chapter 29, pages 161-166.

February 1st, 2014

demography: Forecasting mortality, fertility, migration and population data

The demography package for R contains functions for various demographic analyses. It provides facilities for demographic statistics, modelling and forecasting. […]

October 11th, 2013

Coherent mortality forecasting using functional time series

A talk given today at Macquarie University, Sydney.

August 29th, 2013

ftsa package for R

The ftsa package provides tools for modelling and forecasting functional time series.

August 22nd, 2013

fds package for R

The fds package provides functional data sets useful for testing new methods.

February 1st, 2013

Coherent mortality forecasting: the product-ratio method with functional time series models

Rob J Hyndmana, Heather Boothb and Farah Yasmeena aDepartment of Econometrics & Business Statistics, Monash University, Clayton, Victoria, Australia. bThe […]

January 30th, 2012

Forecasts of COPD mortality in Australia: 2006-2025

Bircan Erbas1, Shahid Ullah2, Rob J Hyndman3, Michelle Scollo4, Michael Abramson5 BMC Medical Research Methodology (2012) 12:17. School of Public […]

July 15th, 2011

Point and interval forecasts of mortality rates and life expectancy: a comparison of ten principal component methods

Han Lin Shang1, Heather Booth2 and Rob J Hyndman1 Department of Econometrics & Business Statistics, Monash University, Clayton, Australia The […]

February 17th, 2011

Nonparametric time series forecasting with dynamic updating

Han Lin Shang and Rob J Hyndman Mathematics and Computers in Simulation (2011), 81, 1310-1324. Abstract We present a nonparametric […]

September 7th, 2010

Demographic forecasting using functional data analysis

University of Wollongong, 8 September 2010. Statistical Society of Australia, Victorian Branch, 28 September 2010. Updated version. September 2012. Abstract: […]

August 3rd, 2010

Exploratory graphics for functional data

Han Lin Shang and Rob J Hyndman Department of Econometrics and Business Statistics, Monash University, Clayton, Australia Interface 2010: Computing Science […]

June 9th, 2010

Coherent functional forecasts of mortality rates and life expectancy

Talk to be given at the International Symposium on Forecasting, San Diego, 20-23 June 2010. Slides

May 6th, 2010

Forecasting age-related changes in breast cancer mortality among white and black US women

Farah Yasmeen, Rob J Hyndman and Bircan Erbas Cancer Epidemiology, 34(5), 542-549. Abstract: The disparity in breast cancer mortality rates […]

March 1st, 2010

Rainbow plots, bagplots and boxplots for functional data

Rob J Hyndman and Han Lin Shang Journal of Computational and Graphical Statistics (2010), 19(1), 29-45. Abstract: We propose new […]

February 6th, 2010

Using functional data analysis models to estimate future time trends of age-specific breast cancer mortality for the United States and England-Wales

Bircan Erbas1, Muhammad Akram2, Dorota M Gertig3, Dallas English4,5, John L. Hopper5, Anne M Kavanagh6 and Rob J Hyndman2 Journal […]

July 24th, 2009

Forecasting functional time series

Rob J Hyndman and Han Lin Shang Journal of the Korean Statistical Society (2009), 38(3), 199-221. (With discussion) Abstract: We […]

July 13th, 2009

Nonparametric time series forecasting with dynamic updating

July 16th, 2008

Stochastic population forecasts using functional data models for mortality, fertility and migration

International Journal of Forecasting (2008), 24(3), 323-342. Rob J Hyndman1 and Heather Booth2 Department of Econometrics and Business Statistics, Monash […]

June 19th, 2008

Bagplots, boxplots and outlier detection for functional data

Australian Statistics Conference. Melbourne, July 2008. When: June 19-21, 2008 Where: First International Workshop on Functional and Operatorial Statistics, Toulouse […]

May 15th, 2008

Bagplots, boxplots and outlier detection for functional data

Rob J Hyndman and Han Lin Shang (2008) In Dabo-Niang, S., and Ferraty, F. (eds), Functional and Operatorial Statistics, chap […]

February 22nd, 2008

Forecasting functional time series

When: 22 February 2008 Where: Australian Frontiers of Science Abstract: Functional time series are curves that are observed sequentially in […]

July 16th, 2007

Robust forecasting of mortality and fertility rates: a functional data approach

Computational Statistics & Data Analysis (2007), 51, 4942-4956. Rob J Hyndman and Md Shahid Ullah Abstract: A new method is […]

February 22nd, 2007

Stochastic population forecasts using functional data models:

When: 12.00noon, Thu 22nd February 2007 Where: Room 213, Richard Berry Building, The University of Melbourne When: 2.30pm, Fri 1 […]

February 16th, 2007

Forecasting age-specific breast cancer mortality using functional data models

Statistics in Medicine (2007), 26(2), 458-470. Bircan Erbas1, Rob J Hyndman2 and Dorota M. Gertig3 Department of Public Health, The […]

October 20th, 2006

Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions

Demographic Research (2006), 15(9), 289-310. Heather Booth1, Rob J. Hyndman2, Leonie Tickle3 and Piet De Jong3 Demography and Sociology Program, […]

April 16th, 2005

Robust forecasting of mortality and fertility rates: a functional data approach

Hyndman, R.J., and Ullah, M.S. (2005) Invited paper, Demographic Forecasting session, 55th session of the International Statistical Institute, Sydney, Australia, […]