论文标题：Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York

发表时间：2022年4-6月

论文所有作者：(1)Lahiri, Kajal(2)Yang, Cheng (通信作者)

期刊名及所属分类：International Journal of Forecasting（SSCI一区）

英文摘要：We forecast New York state tax revenues with a mixed-frequency model using several machine learning techniques. We found that boosting with two dynamic factors extracted from a select list of New York and U.S. leading indicators did best to correctly update revenues for the fiscal year in direct multi-step out-of-sample forecasts. These forecasts were found to be informationally efficient over 18 monthly horizons. In addition to boosting with factors, we also studied the advisability of restricting boosting to select the most recent macro variables to capture abrupt structural changes. Since the COVID-19 pandemic upended all government budgets, our boosted forecasts were used to monitor revenues in real-time for the fiscal year 2021. Our estimates showed a drastic year-over-year decline in actual revenues by over 16% in May 2020, followed by several upward nowcast revisions that led to a recovery of −1% in March 2021, which was close to the actual annual value of −1.6%.

中文摘要：我们将多个机器学习方法在混频模型中预测纽约州的税收。我们发现，在为每个财政年更新直接多步样本外预测时，以从多个纽约与美国先行指标中提取的两个动态因子为预测标量的提升法效果最好。这些预测在18个预测期间中都有信息上的高效性。除了基于因子模型的提升法，我们还建议可以在模型中将宏观变量限制为最近更新的，这样可以更好地抓住结构突变。因为新冠疫情颠覆了所有政府的预算，我们的提升法预测可以对2021财政年的收入进行实时的更新与监测。我们的估计显示，税收的年增长率预测在2020年5月时大幅降为-16%，而经过多次后续的预测更新，最终的年增长率预测回升为-1%，与-1.6%的实际值很接近。