Volume 6 - Article 15 | Pages 409–454
Why population forecasts should be probabilistic - illustrated by the case of Norway
By Nico Keilman, Dinh Quang Pham, Arve Hetland
Abstract
Deterministic population forecasts do not give an appropriate indication of forecast uncertainty. Forecasts should be probabilistic, rather than deterministic, so that their expected accuracy can be assessed.
We review three main methods to compute probabilistic forecasts, namely time series extrapolation, analysis of historical forecast errors, and expert judgement. We illustrate, by the case of Norway up to 2050, how elements of these three methods can be combined when computing prediction intervals for a population’s future size and age-sex composition. We show the relative importance for prediction intervals of various sources of variance, and compare our results with those of the official population forecast computed by Statistics Norway.
Author’s Affiliation
- Nico Keilman - Universitetet i Oslo, Norway EMAIL
- Dinh Quang Pham - Statistisk sentralbyrå (Statistics Norway), Norway EMAIL
- Arve Hetland - Statistisk sentralbyrå (Statistics Norway), Norway EMAIL
Other articles by the same author/authors in Demographic Research
            Editorial: The past, present, and future of Demographic Research
            
                Volume 41 - Article 41
        
            Mortality shifts and mortality compression in period and cohort life tables
            
                Volume 41 - Article 40
        
            Probabilistic household forecasts based on register data- the case of Denmark and Finland
            
                Volume 28 - Article 43
        
            An editorial on plagiarism
            
                Volume 24 - Article 17
        
Most recent similar articles in Demographic Research
            The short- and long-term determinants of fertility in Uruguay
            
                Volume 51 - Article 10
                | Keywords: 
                    fertility,
                    panel data,
                    stages of female reproductive life,
                    time series,
                    Uruguay
        
            Changes in birth seasonality in Spain: Data from 1863–1870 and 1900–2021
            
                Volume 49 - Article 35
                | Keywords: 
                    Box-Jenkins method,
                    Cosinor analysis,
                    Fourier analysis,
                    season of birth,
                    seasonality,
                    time series,
                    vital statistics
        
            The COVID-19 pandemic and fertility responses: TFR simulation analysis using parity progressions in South Korea
            
                Volume 49 - Article 32
                | Keywords: 
                    COVID-19,
                    fertility intentions,
                    marriage intentions,
                    simulation
        
            Variations in male height during the epidemiological transition in Italy: A cointegration approach
            
                Volume 48 - Article 7
                | Keywords: 
                    cointegration analysis,
                    early life conditions,
                    height,
                    historical demography,
                    infant survival,
                    time series
        
            Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth
            
                Volume 47 - Article 8
                | Keywords: 
                    death rates,
                    deep neural network,
                    forecasting,
                    life expectancy
        
Download to Citation Manager
PubMed
Google Scholar