[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v29y2013i2p282-289.html
   My bibliography  Save this article

Evaluating state revenue forecasting under a flexible loss function

Author

Listed:
  • Krol, Robert
Abstract
This paper examines the accuracy of state revenue forecasting under a flexible loss function. Previous research has focused on whether a forecast is rational, meaning that the forecasts are unbiased and the actual forecast errors are uncorrelated with information available at the time of the forecast. These traditional tests assumed that the forecast loss function is quadratic and symmetric. The literature has found that budget forecasts often under-predict revenue and use the available information inefficiently. Using Californian data, I reach the same conclusion using similar tests. However, the rejection of forecast rationality might be the result of an asymmetric loss function. Once the asymmetry of the loss function is taken into account using a flexible loss function, I find evidence that under-forecasting is less costly than over-forecasting California’s revenues. I also find that the forecast errors that take this asymmetry into account are independent of information available at the time of the forecast. These results indicate that a failure to control for possible asymmetry in the loss function in previous work may have produced misleading results.

Suggested Citation

  • Krol, Robert, 2013. "Evaluating state revenue forecasting under a flexible loss function," International Journal of Forecasting, Elsevier, vol. 29(2), pages 282-289.
    • Handle: RePEc:eee:intfor:v:29:y:2013:i:2:p:282-289
    DOI: 10.1016/j.ijforecast.2012.11.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207012001690
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2012.11.003?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. Feenberg, Daniel R, et al, 1989. "Testing the Rationality of State Revenue Forecasts," The Review of Economics and Statistics, MIT Press, vol. 71(2), pages 300-308, May.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Cassidy, Glenn & Kamlet, Mark S. & Nagin, Daniel S., 1989. "An empirical examination of bias in revenue forecasts by state governments," International Journal of Forecasting, Elsevier, vol. 5(3), pages 321-331.
    4. Gentry, William M., 1989. "Do State Revenue Forecasters Utilize Available Information," National Tax Journal, National Tax Association;National Tax Journal, vol. 42(4), pages 429-439, December.
    5. Bretschneider, Stuart I. & Gorr, Wilpen L. & Grizzle, Gloria & Klay, Earle, 1989. "Political and organizational influences on the accuracy of forecasting state government revenues," International Journal of Forecasting, Elsevier, vol. 5(3), pages 307-319.
    6. Alexis Antoniades & Bart Hobijn & Kevin J. Stiroh, 2003. "Taking the pulse of the tech sector: a coincident index of high-tech activity," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 9(Oct).
    7. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1107-1125.
    8. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    9. Batchelor, Roy & Peel, David A., 1998. "Rationality testing under asymmetric loss," Economics Letters, Elsevier, vol. 61(1), pages 49-54, October.
    10. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    11. Bretschneider, Stuart & Gorr, Wilpen, 1992. "Economic, organizational, and political influences on biases in forecasting state sales tax receipts," International Journal of Forecasting, Elsevier, vol. 7(4), pages 457-466, March.
    12. Brown, Bryan W & Maital, Shlomo, 1981. "What Do Economists Know? An Empirical Study of Experts' Expectations," Econometrica, Econometric Society, vol. 49(2), pages 491-504, March.
    13. David H. Romer & Christina D. Romer, 2000. "Federal Reserve Information and the Behavior of Interest Rates," American Economic Review, American Economic Association, vol. 90(3), pages 429-457, June.
    14. Bretschneider, Stuart & Schroeder, Larry, 1988. "Evaluation of commercial economic forecasts for use in local government budgeting," International Journal of Forecasting, Elsevier, vol. 4(1), pages 33-43.
    15. Mocan, H. Naci & Azad, Sam, 1995. "Accuracy and rationality of state General Fund Revenue forecasts: Evidence from panel data," International Journal of Forecasting, Elsevier, vol. 11(3), pages 417-427, September.
    16. 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.
    17. Gentry, William M., 1989. "Do State Revenue Forecasters Utilize Available Information," National Tax Journal, National Tax Association, vol. 42(4), pages 429-39, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chatagny, Florian, 2015. "Incentive effects of fiscal rules on the finance minister's behavior: Evidence from revenue projections in Swiss Cantons," European Journal of Political Economy, Elsevier, vol. 39(C), pages 184-200.
    2. Kirdan Lees, 2016. "Assessing forecast performance," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 79, pages 1-19., June.
    3. Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
    4. Robert Krol, 2014. "Forecast Bias of Government Agencies," Cato Journal, Cato Journal, Cato Institute, vol. 34(1), pages 99-112, Winter.
    5. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    6. 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.
    7. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    8. Madina Serikova & Lyazzat Sembiyeva & Kuralay Balginova & Gulzhan Alina & Aliya Shakharova & Anar Kurmanalina, 2020. "Tax revenues estimation and forecast for state tax audit," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(3), pages 2419-2435, March.
    9. Barbara Batóg & Jacek Batóg, 2021. "Regional Government Revenue Forecasting: Risk Factors of Investment Financing," Risks, MDPI, vol. 9(12), pages 1-15, November.
    10. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, vol. 12(15), pages 1-24, July.
    11. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    12. Fildes, Robert, 2015. "Forecasters and rationality—A comment on Fritsche et al., Forecasting the Brazilian Real and Mexican Peso: Asymmetric loss, forecast rationality and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 140-143.

    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. repec:rre:publsh:v:33:y:2003:i:1:p:85-103 is not listed on IDEAS
    2. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    3. Mocan, H. Naci & Azad, Sam, 1995. "Accuracy and rationality of state General Fund Revenue forecasts: Evidence from panel data," International Journal of Forecasting, Elsevier, vol. 11(3), pages 417-427, September.
    4. 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.
    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. Hamid Baghestani, 2013. "Evaluating Federal Reserve predictions of growth in consumer spending," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1637-1646, May.
    7. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    8. Veress, Aron & Kaiser, Lars, 2017. "Forecasting quality of professionals: Does affiliation matter?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 159-168.
    9. 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.
    10. 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.
    11. Vasconcelos de Deus, Joseph David Barroso & de Mendonça, Helder Ferreira, 2017. "Fiscal forecasting performance in an emerging economy: An empirical assessment of Brazil," Economic Systems, Elsevier, vol. 41(3), pages 408-419.
    12. 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.
    13. Chatagny, Florian, 2015. "Incentive effects of fiscal rules on the finance minister's behavior: Evidence from revenue projections in Swiss Cantons," European Journal of Political Economy, Elsevier, vol. 39(C), pages 184-200.
    14. Hamid Baghestani, 2014. "On the loss structure of federal reserve forecasts of output growth," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 38(3), pages 518-527, July.
    15. Baghestani, Hamid & Khallaf, Ashraf, 2012. "Predictions of growth in U.S. corporate profits: Asymmetric vs. symmetric loss," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 222-229.
    16. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    17. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    18. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    19. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric loss functions and the rationality of expected stock returns," International Journal of Forecasting, Elsevier, vol. 27(2), pages 413-437.
    20. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    21. 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.

    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:intfor:v:29:y:2013:i:2:p:282-289. 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/ijforecast .

    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.