IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v244y2024ics0165176524005196.html
   My bibliography  Save this article

The efficiency of the Japanese government’s revenue projections

Author

Listed:
  • Arai, Natsuki
  • Iizuka, Nobuo
  • Yamamoto, Yohei

Abstract

This paper evaluates the efficiency of the Japanese fiscal authority’s revenue projections using real-time data from 1960 to 2022. While their one-year-ahead projections are not efficient, their accuracy can be significantly improved by adjusting the forecasts based on the results.

Suggested Citation

  • Arai, Natsuki & Iizuka, Nobuo & Yamamoto, Yohei, 2024. "The efficiency of the Japanese government’s revenue projections," Economics Letters, Elsevier, vol. 244(C).
  • Handle: RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524005196
    DOI: 10.1016/j.econlet.2024.112035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176524005196
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2024.112035?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pina, Álvaro M. & Venes, Nuno M., 2011. "The political economy of EDP fiscal forecasts: An empirical assessment," European Journal of Political Economy, Elsevier, vol. 27(3), pages 534-546, September.
    2. Jacopo Cimadomo, 2016. "Real-Time Data And Fiscal Policy Analysis: A Survey Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 302-326, April.
    3. Ashiya, Masahiro, 2007. "Forecast accuracy of the Japanese government: Its year-ahead GDP forecast is too optimistic," Japan and the World Economy, Elsevier, vol. 19(1), pages 68-85, January.
    4. Croushore, Dean & van Norden, Simon, 2019. "Fiscal Surprises at the FOMC," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1583-1595.
    5. Teresa Leal & Javier J. Pérez & Mika Tujula & Jean-Pierre Vidal, 2008. "Fiscal Forecasting: Lessons from the Literature and Challenges," Fiscal Studies, Institute for Fiscal Studies, vol. 29(3), pages 347-386, September.
    6. 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.
    7. Andrew Patton & Allan Timmermann, 2012. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17.
    8. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    9. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    10. Susanna-maria Paleologou, 2005. "Political manoeuvrings as sources of measurement errors in forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(5), pages 311-324.
    11. Thiess Buettner & Bjoern Kauder, 2010. "Revenue Forecasting Practices: Differences across Countries and Consequences for Forecasting Performance," Fiscal Studies, Institute for Fiscal Studies, vol. 31(3), pages 313-340, September.
    12. Tsuchiya, Yoichi, 2016. "Assessing macroeconomic forecasts for Japan under an asymmetric loss function," International Journal of Forecasting, Elsevier, vol. 32(2), pages 233-242.
    13. Dean Croushore & Simon van Norden, 2018. "Fiscal Forecasts at the FOMC: Evidence from the Greenbooks," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 933-945, December.
    14. Campbell, Bryan & Ghysels, Eric, 1995. "Federal Budget Projections: A Nonparametric Assessment of Bias and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 17-31, February.
    15. Auerbach, Alan J., 1999. "On the Performance and Use of Government Revenue Forecasts," National Tax Journal, National Tax Association;National Tax Journal, vol. 52(4), pages 765-782, December.
    16. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    17. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1.
    18. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 17, pages 961-982, Elsevier.
    19. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    20. Kevin L. Kliesen & Daniel L. Thornton, 2001. "The expected federal budget surplus: how much confidence should the public and policymakers place in the projections?," Review, Federal Reserve Bank of St. Louis, vol. 83(Mar), pages 11-24.
    21. Ashiya, Masahiro, 2003. "Testing the rationality of Japanese GDP forecasts: the sign of forecast revision matters," Journal of Economic Behavior & Organization, Elsevier, vol. 50(2), pages 263-269, February.
    22. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    23. Martinez, Andrew B., 2015. "How good are US government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 31(2), pages 312-324.
    24. Satoko Maekawa & Mototsugu Fukushige, 2012. "Tax Projections And Economic Forecasts By Government Bureaucrats: Hidden Manoeuverings Behind Fiscal Reconstruction In Japan," The Japanese Economic Review, Japanese Economic Association, vol. 63(4), pages 528-545, December.
    25. Reifschneider, David & Tulip, Peter, 2019. "Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1564-1582.
    26. Kenneth Eva & Fabian Winkler, 2023. "A Comprehensive Empirical Evaluation of Biases in Expectation Formation," Finance and Economics Discussion Series 2023-042, Board of Governors of the Federal Reserve System (U.S.).
    27. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    28. Arai, Natsuki, 2014. "Using forecast evaluation to improve the accuracy of the Greenbook forecast," International Journal of Forecasting, Elsevier, vol. 30(1), pages 12-19.
    29. David L. Reifschneider & Peter Tulip, 2007. "Gauging the uncertainty of the economic outlook from historical forecasting errors," Finance and Economics Discussion Series 2007-60, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arai, Natsuki & Iizuka, Nobuo & Yamamoto, Yohei, 2022. "The Efficiency of the Government’s Revenue Projections," Discussion paper series HIAS-E-122, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    2. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.
    3. Björn Kauder & Niklas Potrafke & Christoph Schinke, 2017. "Manipulating Fiscal Forecasts: Evidence from the German States," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 73(2), pages 213-236, June.
    4. Joan Paredes & Javier J. Pérez & Gabriel Perez Quiros, 2023. "Fiscal targets. A guide to forecasters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 472-492, June.
    5. Beate Jochimsen & Robert Lehmann, 2017. "On the political economy of national tax revenue forecasts: evidence from OECD countries," Public Choice, Springer, vol. 170(3), pages 211-230, March.
    6. Boukari, Mamadou & Veiga, Francisco José, 2018. "Disentangling political and institutional determinants of budget forecast errors: A comparative approach," Journal of Comparative Economics, Elsevier, vol. 46(4), pages 1030-1045.
    7. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    8. Dean Croushore & Simon van Norden, 2018. "Fiscal Forecasts at the FOMC: Evidence from the Greenbooks," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 933-945, December.
    9. Zidong An & Joao Tovar Jalles, 2020. "On the performance of US fiscal forecasts: government vs. private information," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(2), pages 367-391, June.
    10. António Afonso & Rui Carvalho, 2014. "Revenue Forecast Errors in the European Union," Working Papers Department of Economics 2014/02, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    11. Cronin, David & McGowan, Kieran, 2023. "Government debt forecast errors and the net expenditure rule in EU countries," Papers WP756, Economic and Social Research Institute (ESRI).
    12. Merola, Rossana & Pérez, Javier J., 2013. "Fiscal forecast errors: Governments versus independent agencies?," European Journal of Political Economy, Elsevier, vol. 32(C), pages 285-299.
    13. Arai, Natsuki, 2014. "Using forecast evaluation to improve the accuracy of the Greenbook forecast," International Journal of Forecasting, Elsevier, vol. 30(1), pages 12-19.
    14. David Cronin & Niall McInerney, 2024. "Institutional Quality and Official Budgetary Forecast Performance in EU Member States," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 80(2), pages 165-192.
    15. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    16. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.
    17. Rossana Merola & Javier J. Pérez, 2012. "Fiscal forecast errors: governments vs independent agencies?," Working Papers 1233, Banco de España.
    18. Cronin, David & McQuinn, Kieran, 2023. "Government debt forecast errors and the net expenditure rule in EU countries: Undue optimism at a cost," Journal of Policy Modeling, Elsevier, vol. 45(6), pages 1113-1131.
    19. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    20. Cronin, David & McInerney, Niall, 2023. "Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment," European Journal of Political Economy, Elsevier, vol. 76(C).

    More about this item

    Keywords

    Revenue projections; Japan; Forecast evaluation; Real-time data; Out-of-sample forecast accuracy;
    All these keywords.

    JEL classification:

    • 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
    • H68 - Public Economics - - National Budget, Deficit, and Debt - - - Forecasts of Budgets, Deficits, and Debt

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524005196. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.