Larson William D, Sinclair Tara M
Federal Housing Finance Agency, United States of America.
George Washington University, United States of America.
Int J Forecast. 2022 Apr-Jun;38(2):635-647. doi: 10.1016/j.ijforecast.2021.01.001. Epub 2021 Jan 11.
Near-term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. Our results suggest that in times of structural change there is a bias-variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.
短期预测，也称为临近预报，在经济经历突变时最具挑战性，但也最为重要。在本文中，我们探讨了具有不同信息集和数据结构的模型的性能，以便在2020年春季新冠疫情期间对美国首次申领失业救济人数进行最佳临近预报。我们表明，最佳模型，尤其是在申领人数出现结构性断点附近时，是一个州级面板模型，该模型包含虚拟变量以捕捉紧急声明发布时间的变化。自回归模型起初表现不佳，但相对较快地迎头赶上。利用紧急声明发布时间变化的州级面板模型，其表现也优于包含谷歌趋势的模型。我们的结果表明，在结构变化时期存在偏差 - 方差权衡。早期，利用横截面维度中的相关信息的简单方法可改善预测，但在后期，自回归模型的效率占主导地位。
