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<p>Forecasting human development is important for tracking sustainable growth and societal progress. However, this task presents statistical challenges. The primary difficulty is the limited nature of the available data, which is a typical problem encountered in forecasting many social time series. In this paper, we propose a novel approach for forecasting short time series based on the Theta method. The classical Theta method decomposes the time series into trend and short-run components. We propose an improved version of the Theta method, called <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-comb, based on the combination of alternative forecasts for the short-run component. We apply the proposed method to forecast worldwide human development, measured with the Human Development Index, from 1990 to 2022. The results show that the <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\theta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>θ</mi> </math></EquationSource> </InlineEquation>-comb method significantly improves the out-of-sample accuracy in comparison to existing approaches.</p>

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Forecasting human development with an improved Theta method based on forecast combination

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月29日

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  • Raffaele Mattera,
  • Germana Scepi,
  • Parmjit Kaur

摘要

Forecasting human development is important for tracking sustainable growth and societal progress. However, this task presents statistical challenges. The primary difficulty is the limited nature of the available data, which is a typical problem encountered in forecasting many social time series. In this paper, we propose a novel approach for forecasting short time series based on the Theta method. The classical Theta method decomposes the time series into trend and short-run components. We propose an improved version of the Theta method, called \(\theta \) θ -comb, based on the combination of alternative forecasts for the short-run component. We apply the proposed method to forecast worldwide human development, measured with the Human Development Index, from 1990 to 2022. The results show that the \(\theta \) θ -comb method significantly improves the out-of-sample accuracy in comparison to existing approaches.