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<p>In this paper we forecast social security disability applications in the USA using a spatial dynamic panel data model. Specifically, we forecast three types of applications—those who filed under (i) the Social Security Disability Insurance (SSDI), (ii) the Supplemental Security Income (SSI), and (iii) simultaneously (Concurrent). We find strong cross-sectional correlations in claim applications across states, which is exploited in forecasting. Depending on the forecast horizons from 1 to 12&#xa0;months, the improvement in out-of-sample state level forecast accuracies as measured by root mean squared error (RMSE) is substantial, and find that the contemporaneous spatial lag term has the greatest contribution to the improvement. The gain from the spatial lag increases with forecast horizon—at the 9-month horizon the aggregate RMSE is reduced by almost 28% due to the utilization of spatial lag in forecasting.</p>

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Forecasting U.S. social security disability applications: a spatial dynamic panel data model approach

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Kajal Lahiri,
  • Cheng Yang,
  • Yimeng Yin

摘要

In this paper we forecast social security disability applications in the USA using a spatial dynamic panel data model. Specifically, we forecast three types of applications—those who filed under (i) the Social Security Disability Insurance (SSDI), (ii) the Supplemental Security Income (SSI), and (iii) simultaneously (Concurrent). We find strong cross-sectional correlations in claim applications across states, which is exploited in forecasting. Depending on the forecast horizons from 1 to 12 months, the improvement in out-of-sample state level forecast accuracies as measured by root mean squared error (RMSE) is substantial, and find that the contemporaneous spatial lag term has the greatest contribution to the improvement. The gain from the spatial lag increases with forecast horizon—at the 9-month horizon the aggregate RMSE is reduced by almost 28% due to the utilization of spatial lag in forecasting.