Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/248910 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 9365
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
We forecast New York state tax revenues with a mixed-frequency model using a number of machine learning techniques. We found boosting with two dynamic factors extracted from a select list of New York and U.S. leading indicators did best in terms of correctly updating 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 real revenues by over 16% in May 2020, followed by several upward nowcast revisions that led to a recovery to -1% in March 2021, which was close to the actual annual value of -1.6%.
Subjects: 
revenue forecasting
machine learning
real time forecasting
mixed frequency
fiscal policy
JEL: 
C22
C32
C50
C53
E62
Document Type: 
Working Paper
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