Boosting Tax Revenues with Mixed-Frequency Data in the Aftermath of Covid-19: The Case of New York

Kajal Lahiri and Cheng Yang

No 9365, CESifo Working Paper Series from CESifo

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%.

Keywords: revenue forecasting; machine learning; real time forecasting; mixed frequency; fiscal policy (search for similar items in EconPapers)
JEL-codes: C22 C32 C50 C53 E62 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-mac and nep-pub
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Journal Article: Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_9365

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