Abstract
The Congressional Budget Office (CBO) serves as the forecasting agency for the United States Congress. Forecast accuracy is important because the fiscal projections set the parameters for deliberations over taxing and spending policies. Thus, it is necessary to assess the ability of the CBO to use information effectively to make quality projections. We do so by examining whether the CBO’s updated one-year ahead and five-year cumulative projections are more accurate than its initial projections for fiscal years 1978 through 2017. We find that the updated projections are generally more accurate, suggesting that the CBO is effectively using the new information it collects over a short period of time (generally 6–7 months) to improve the quality of its forecasts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In the international context, establishing independent legislative budget offices is an important trend in the Organisation for Economic Co-operation and Development (OECD) countries, especially in the aftermath of the fiscal crises experienced by many countries (Chohan 2018; Kim 2015; Von Trapp et al. 2016). In existing comparative studies, the CBO is usually ranked first in terms of its independence and analytical capacities (Von Trapp et al. 2016; see also Kim 2015; Wehner 2006). In that light, the CBO can be viewed as the “most likely case” to provide accurate predictions; in other words, if the CBO has problems with accuracy, the other legislative budget offices in the world are likely to face even more severe challenges in forecasting.
- 2.
This is not surprising given that economists from the CBO, OMB, and Council of Economic Advisors work together to produce a consensus forecast for the economy and CBO “does not want to deviate far from the consensus” (Penner 2002: 8).
- 3.
An updated forecast for the current fiscal year (in this example, 2017) is also provided.
- 4.
Examples of technical changes include “modeling improvements, the incorporation of new demographic information, recent agency actions or judicial decisions, and updated data from federal agencies or other sources” (CBO2017: 3).
- 5.
Two studies have assessed updated one-year ahead forecasts in Europe. Carabotta (2014) found that autumn forecasts of Italy’s deficit are generally more accurate than the initial spring forecasts, and Pérez (2007) found that updated forecasts of deficits in Eurozone countries tend to outperform initial projections. Both authors attribute the improvements to better information being available at the time of the updated forecasts.
- 6.
Projection error = et = \( U(x)=\alpha +\beta x\;\mathrm{with}\;\beta >0 \); Mean error = \( \frac{1}{N}\sum \limits_{i=1}^NU\left({x}_i\right)=\frac{1}{N}\sum \limits_{i=1}^N\left(\alpha +\beta {x}_i\right)=\alpha +\frac{1}{N}\beta \sum \limits_{i=1}^N{x}_i \); MAE = \( \overline{X} \); RMSE = \( \overline{U}\left({x}_i\right)=\alpha +\beta \overline{X} \)
- 7.
The difference in the absolute errors = |initial et| − |update et|
The difference in the squared errors = (initial et)2 − (update et)2
- 8.
Deficits are recorded as negative numbers. Thus, if the projected deficit is −$100 billion and the actual deficit is −$110 billion, the error would be +$10 billion (−100 minus −110 = 10).
- 9.
Remember that positive errors signify that the deficit was larger than projected.
- 10.
References
Auerbach, A. J. (1999). On the performance and use of government revenue forecasts. National Tax Journal, 52(4), 767–782.
Beetsma, R., Giuliodori, M., & Wierts, P. (2009). Planning to cheat: EU fiscal policy in real time. Economic Policy, 24(60), 753–804.
Bretschneider, S. I., & Gorr, W. L. (1992). Economic, organizational, and political influences on biases in forecasting state sales tax receipts. International Journal of Forecasting, 7(4), 457–466. https://doi.org/10.1016/0169-2070(92)90029-9.
Carabotta, L. (2014). Which agency and which period is the best? Analyzing national and international fiscal forecasts in Italy. International Journal of Economic Sciences, 3(1), 27–46.
Chohan, U. W. (2018). Independent budget offices and the politics–administration dichotomy. International Journal of Public Administration, 41(12), 1009–1017.
Congressional Budget Office (CBO). (2017). An evaluation of CBO’s past outlay projections. Congress of the United States. Washington, DC. Retrieved from https://www.cbo.gov/publication/53328.
de Deus, J. D. B. V., & de Mendonça, H. F. (2017). Fiscal forecasting performance in an emerging economy: An empirical assessment of Brazil. Economic Systems, 41(3), 408–419.
Ericsson, N. R. (2017). How biased are US government forecasts of the federal debt? International Journal of Forecasting, 33(2), 543–559.
Joyce, P. G. (2011). The Congressional Budget Office: Honest numbers, power, and policymaking. Washington, DC: Georgetown University Press.
Kim, C. (2015). A study on compilation and improvement of indices for legislative budgetary institutions. OECD Journal on Budgeting, 14(3), 1–29.
Kliesen, K. L., & Thornton, D. L. (2012). How good are the government’s deficit and debt projections and should we care? Federal Reserve Bank of St Louis Review, 94(1), 21–39.
Krause, G. A., & Douglas, J. W. (2005). Institutional design versus reputational effects on bureaucratic performance: Evidence from US government macroeconomic and fiscal projections. Journal of Public Administration Research and Theory, 15(2), 281–306.
Krause, G. A., & Douglas, J. W. (2006). Does agency competition improve the quality of policy analysis? Evidence from OMB and CBO fiscal projections. Journal of Policy Analysis and Management, 25(1), 53–74.
Krause, G. A., & Douglas, J. W. (2013). Organizational structure and the optimal design of policymaking panels: Evidence from consensus group commissions’ revenue forecasts in the American states. American Journal of Political Science, 57(1), 135–149.
Martinez, A. B. (2015). How good are US government forecasts of the federal debt? International Journal of Forecasting, 31(2), 312–324.
Mikesell, J. L. (2018). Often wrong, never uncertain: Lessons from 40 years of state revenue forecasting. Public Administration Review, 78(5), 795–802.
Penner, R. G. (2002). Dealing with uncertain budget forecasts. Public Budgeting & Finance, 22(1), 1–18.
Pérez, J. J. (2007). Leading indicators for euro area government deficits. International Journal of Forecasting, 23(2), 259–275.
Pina, Á. M., & Venes, N. M. (2011). The political economy of EDP fiscal forecasts: An empirical assessment. European Journal of Political Economy, 27, 534–546. https://doi.org/10.1016/j.ejpoleco.2011.01.005.
Plesko, G. A. (1988). The accuracy of government forecasts and budget projections. National Tax Journal, 41(4), 483–501.
Reitano, V. (2018). An open systems model of local government forecasting. The American Review of Public Administration, 48(5), 476–489.
Rodgers, R., & Joyce, P. G. (1996). The effect of underforecasting on the accuracy of revenue forecasts by state governments. Public Administration Review, 56(1), 48–56.
Von Trapp, L., Lienert, I., & Wehner, J. (2016). Principles for independent fiscal institutions and case studies. OECD Journal on Budgeting, 15(2), 9–24.
Wehner, J. (2006). Assessing the power of the purse: An index of legislative budget institutions. Political Studies, 54(4), 767–785.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 The Author(s)
About this chapter
Cite this chapter
Douglas, J.W., Raudla, R. (2019). CBO Updated Forecasts: Do a Few Months Matter?. In: Williams, D., Calabrese, T. (eds) The Palgrave Handbook of Government Budget Forecasting. Palgrave Studies in Public Debt, Spending, and Revenue. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-18195-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-18195-6_7
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-18194-9
Online ISBN: 978-3-030-18195-6
eBook Packages: Economics and FinanceEconomics and Finance (R0)