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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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  • Kajal Lahiri
  • Cheng Yang

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%.

Suggested Citation

  • Kajal Lahiri & Cheng Yang, 2021. "Boosting Tax Revenues with Mixed-Frequency Data in the Aftermath of Covid-19: The Case of New York," CESifo Working Paper Series 9365, CESifo.
    • Handle: RePEc:ces:ceswps:_9365
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    1. Jesus Fernandez-Villaverde & Charles I. Jones, 2020. "Macroeconomic Outcomes and COVID-19: A Progress Report," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(3 (Fall)), pages 111-166.
    2. Bo Zhao, 2020. "Forecasting the New England States’ Tax Revenues in the Time of the COVID-19 Pandemic," Current Policy Perspectives 88356, Federal Reserve Bank of Boston.
    3. Michael W. McCracken & Joseph T. McGillicuddy, 2019. "An empirical investigation of direct and iterated multistep conditional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 181-204, March.
    4. Jeffrey Clemens & Stan Veuger, 2020. "Implications of the COVID-19 Pandemic for State Government Tax Revenues," National Tax Journal, National Tax Association;National Tax Journal, vol. 73(3), pages 619-644, September.
    5. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    6. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    7. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
    8. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    9. Jeffrey Frankel, 2011. "Over-optimism in forecasts by official budget agencies and its implications," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 27(4), pages 536-562.
    10. Serena Ng, 2014. "Viewpoint: Boosting Recessions," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 47(1), pages 1-34, February.
    11. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
    12. Buhlmann P. & Yu B., 2003. "Boosting With the L2 Loss: Regression and Classification," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 324-339, January.
    13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    14. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    15. Megna, Robert & Xu, Qiang, 2003. "Forecasting the New York State economy: The coincident and leading indicators approach," International Journal of Forecasting, Elsevier, vol. 19(4), pages 701-713.
    16. Makridis, Christos & McNab, Robert, 2020. "The Fiscal Cost of COVID-19: Evidence from the States," Working Papers 10702, George Mason University, Mercatus Center.
    17. Onorante, Luca & Pedregal, Diego J. & Pérez, Javier J. & Signorini, Sara, 2010. "The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area," Journal of Policy Modeling, Elsevier, vol. 32(1), pages 98-119, January.
    18. repec:aei:rpaper:1008570714 is not listed on IDEAS
    19. Steven Lehrer & Tian Xie & Tao Zeng, 2021. "Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures? [Regression Models with Mixed Sampling Frequencies]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 910-933.
    20. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    21. Baicker, Katherine & Clemens, Jeffrey & Singhal, Monica, 2012. "The rise of the states: U.S. fiscal decentralization in the postwar period," Journal of Public Economics, Elsevier, vol. 96(11), pages 1079-1091.
    22. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    23. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    24. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    25. Pedregal, Diego J. & Pérez, Javier J., 2010. "Should quarterly government finance statistics be used for fiscal surveillance in Europe?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 794-807, October.
    26. Benjamin Hofner & Andreas Mayr & Nikolay Robinzonov & Matthias Schmid, 2014. "Model-based boosting in R: a hands-on tutorial using the R package mboost," Computational Statistics, Springer, vol. 29(1), pages 3-35, February.
    27. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    28. Davig, Troy & Hall, Aaron Smalter, 2019. "Recession forecasting using Bayesian classification," International Journal of Forecasting, Elsevier, vol. 35(3), pages 848-867.
    29. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2021. "COVID-19 and the Costs of Deadly Disasters," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 366-370, May.
    30. Krol, Robert, 2013. "Evaluating state revenue forecasting under a flexible loss function," International Journal of Forecasting, Elsevier, vol. 29(2), pages 282-289.
    31. Daniel Lewis & Karel Mertens & James H. Stock, 2020. "U.S. Economic Activity During the Early Weeks of the SARS-Cov-2 Outbreak," NBER Working Papers 26954, National Bureau of Economic Research, Inc.
    32. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    33. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    34. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    35. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    36. John E. Anderson & Shafiun N. Shimul, 2018. "State and Local Property, Income, and Sales Tax Elasticity: Estimates from Dynamic Heterogeneous Panels," National Tax Journal, National Tax Association;National Tax Journal, vol. 71(3), pages 521-546, September.
    37. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
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    Cited by:

    1. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.

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    More about this item

    Keywords

    revenue forecasting; machine learning; real time forecasting; mixed frequency; fiscal policy;
    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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