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

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  • Lahiri, Kajal
  • Yang, Cheng

Abstract

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%.

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  • Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:2:p:545-566
    DOI: 10.1016/j.ijforecast.2021.10.005
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    More about this item

    Keywords

    Boosting; Tax revenue; Machine learning; MIDAS; Forecast efficiency; COVID-19 Pandemic; DMS forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

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