Population Forecast Errors

Rayer, Stefan

doi:10.1177/0739456x07313925

Cited by 51 publications

(29 citation statements)

References 49 publications

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“…The absolute error increases dramatically with starting population size, while the absolute relative error (Eq. 2) is highest for smaller countries, consistent with findings from other studies (National Research Council 2000;Rayer 2008). For example, India's population was underpredicted by more than 40 million people, but this represents less than 4% of the total population.…”

Section: Resultssupporting

confidence: 82%

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A probabilistic interpretation of the United Nations’ 1995–2005 population projections

Murtaugh

2010

J Pop Research

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I develop probabilistic interpretations for the United Nations' 10-year population forecasts by comparing 1995 projections for 212 countries to the population sizes reported for 2005. Errors in the estimation of the intrinsic rate of increase, presumably caused by erroneous assumptions about birth, death and/or immigration rates, appear to be more consequential than errors based on inaccurate estimation of the starting, or 'jump-off', population size. For only about 20% of the countries did the 'actual' 2005 population size fall between the United Nations' low-and high-variant projections. I propose prediction intervals for country-specific population sizes 10 years in the future of the form ½N 0 i ðt þ 10Þ=k; k Á N 0 i ðt þ 10Þ; where N i 0 (t ? 10) is the medium-variant prediction for year t ? 10 made in year t, and k is a number that varies with starting population size. Based on the 1995-2005 United Nations' data, values of k giving 95% coverage range from 1.11 for countries with a population on the order of 10 9 , to 1.45 for countries with a population of 10 5 .

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Section: Resultssupporting

confidence: 82%

“…trends to complicated modelling that incorporates expert opinion about changes in fertility, mortality and migration (Booth 2006;Rayer 2008). Historically, most projections were deterministic, but there has been a recent impetus to develop and use probabilistic forecasting methods.…”

mentioning

confidence: 99%

A probabilistic interpretation of the United Nations’ 1995–2005 population projections

Murtaugh

2010

J Pop Research

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“…We conducted our analyses using a data set covering all counties in the US that did not experience significant boundary changes between 1900 and 2000 (Rayer 2008). This data set included 2,482 counties, 79% of the national total.…”

Section: Datamentioning

confidence: 99%

“…Forecasts for each launch year were derived from five extrapolation techniques: linear, exponential, share of growth, shift share, and constant share (Rayer 2008). The forecasts analyzed in this study were calculated as an average of the forecasts from these five techniques, after excluding the highest and lowest.…”

Section: Datamentioning

confidence: 99%

“…Measuring population size in the launch year and growth rate over the base period, these studies have generally found precision to improve with increases in population size and decline with increases in the absolute value of the growth rate (e.g., Keyfitz 1981; Rayer 2008; Smith and Sincich 1992; Stoto 1983; White 1954). They have found bias to have little or no relationship with population size but to be positively related to the growth rate (e.g., Isserman 1977; Rayer 2008; Smith 1987; Tayman 1996). These results have been found so frequently that we believe they can be accepted as general characteristics of population forecast errors.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Population Forecast Accuracy: A Regression Approach Using County Data

Tayman

Smith

Rayer

2010

Popul Res Policy Rev

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Many studies have evaluated the impact of differences in population size and growth rate on population forecast accuracy. Virtually all these studies have been based on aggregate data; that is, they focused on average errors for places with particular size or growth rate characteristics. In this study, we take a different approach by investigating forecast accuracy using regression models based on data for individual places. Using decennial census data from 1900 to 2000 for 2,482 counties in the US, we construct a large number of county population forecasts and calculate forecast errors for 10- and 20-year horizons. Then, we develop and evaluate several alternative functional forms of regression models relating population size and growth rate to forecast accuracy; investigate the impact of adding several other explanatory variables; and estimate the relative contributions of each variable to the discriminatory power of the models. Our results confirm several findings reported in previous studies but uncover several new findings as well. We believe regression models based on data for individual places provide powerful but under-utilized tools for investigating the determinants of population forecast accuracy.

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Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model

Yang

et al. 2017

Journal of Forecasting

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China is a populous country that is facing serious aging problems due to the single‐child birth policy. Debate is ongoing whether the liberalization of the single‐child policy to a two‐child policy can mitigate China's aging problems without unacceptably increasing the population. The purpose of this paper is to apply machine learning theory to the demographic field and project China's population structure under different fertility policies. The population data employed derive from the fifth and sixth national census records obtained in 2000 and 2010 in addition to the annals published by the China National Bureau of Statistics. Firstly, the sex ratio at birth is estimated according to the total fertility rate based on least squares regression of time series data. Secondly, the age‐specific fertility rates and age‐specific male/female mortality rates are projected by a least squares support vector machine (LS‐SVM) model, which then serve as the input to a Leslie matrix model. Finally, the male/female age‐specific population data projected by the Leslie matrix in a given year serve as the input parameters of the Leslie matrix for the following year, and the process is iterated in this manner until reaching the target year. The experimental results reveal that the proposed LS‐SVM‐Leslie model improves the projection accuracy relative to the conventional Leslie matrix model in terms of the percentage error and mean algebraic percentage error. The results indicate that the total fertility ratio should be controlled to around 2.0 to balance concerns associated with a large population with concerns associated with an aging population. Therefore, the two‐child birth policy should be fully instituted in China. However, the fertility desire of women tends to be low due to the high cost of living and the pressure associated with employment, particularly in the metropolitan areas. Thus additional policies should be implemented to encourage fertility.

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Population Forecast Errors

Cited by 51 publications

References 49 publications

A probabilistic interpretation of the United Nations’ 1995–2005 population projections

A probabilistic interpretation of the United Nations’ 1995–2005 population projections

Evaluating Population Forecast Accuracy: A Regression Approach Using County Data

Projection of population structure in China using least squares support vector machine in conjunction with a Leslie matrix model

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