Factors determining the forecast errors of market analysts for fiscal variables in Brazil
DOI:
https://doi.org/10.18593/race.21417Keywords:
Forecast error, Public finances, Prisma Fiscal, MAPE. z.Abstract
The objective of this study is to investigate determinant factors the forecast errors of market analysts for Brazilian fiscal variables. The data for conducting the research was obtained in the Prisma Fiscal, the Ministry of Economy's system of collecting and disclosure of market expectations for fiscal variables. The data collected refer to collection, net revenue and total expenditure, in the period from November 2015 to December 2018. The Mean Absolute Percentage Error (MAPE) and z were used to measure the quality of the market analysts' forecast. The use of the z value as a measure of the forecast error is one of the contributions of this research. Among the results obtained, the hypothesis that the temporal horizon interferes in the quality of the forecast was not rejected for horizons of one and two years; the dispersion of forecasts did not show a substantial change; and the optimistic bias hypothesis was not confirmed. It can be concluded that for this sample the temporality is a determinant factor of the forecast error of the market analysts for fiscal variables. The research contributes to the discussion about forecasting error in the areas of Public Financial Management and Public Accounting.
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References
An, Z., Jalles, J. T., Loungani, P., & Sousa, R. M. (2018). Do IMF fiscal forecasts add value?
Journal of Forecasting, 37, 650-665.
Armstrong, J. S., & Collopy, F. (1992). Error Measures for Generalizing About Forecasting
Methods: Empirical Comparisons. International Journal of Forecasting, 8(1), 69-80.
Asimakopoulos, S., Paredes, J., & Warmedinger, T. (2020). Real-Time Fiscal
Forecasting Using Mixed-Frequency Data. Scandinavian Journal of Economics, 122(1), 369-390.
Banco Central do Brasil. (2018). Estoque de swap cambial caiu nos últimos dois anos.
Recuperado de https://www.bcb.gov.br/noticiasporano
Banco Central do Brasil. (2019). Focus Relatório de Mercado. Recuperado de
https://www.bcb.gov.br/publicacoes/focus
Brasil. Ministério da Economia. (2018). Prisma Fiscal—Nota Metodológica. Recuperado de http://www.fazenda.gov.br/prisma-fiscal/prisma-fiscal
Brasil. Ministério da Fazenda. (2016). Resultado do Tesouro Nacional. Secretaria do Tesouro
Nacional, 2(10). Recuperado de http://www.planalto.gov.br/ccivil_03/decreto-lei/del0200.htm
Brogan, M. (2012). The politics of budgeting: evaluating the effects of the political election
cycle on state-level budget forecast errors. Journal Public Administration, 36(1), 84-115.
Clement, M. (1999). Analyst forecast accuracy. Journal of Accounting and Economics, 27(3), 285-303.
Conselho Federal de Contabilidade. (2016). NBC TSP—Estrutura Conceitual para Elaboração e Divulgação de Informação Contábil de Propósito Geral pelas Entidades do Setor Público. Recuperado de http://www2.cfc.org.br/sisweb/sre/detalhes_sre.aspx?Codigo=2016/NBCTSPEC&arquivo=NBCTSPEC.doc
Costa, A. E., Filho, & Rocha, F. (2010). Como o mercado de juros futuros reage à
comunicação do Banco Central? Economia aplicada, 14(3), 265-292.
Costa, E. A. A. (2011). Fatores institucionais que influenciam a previsão das receitas
orçamentárias: Um estudo de caso dos governos estuais brasileiros (Dissertação de
mestrado). Universidade de Brasília, Brasília, DF.
Deus, J. D. B. V., & Mendonça, H. F. (2017). Fiscal forecasting performance in an emerging
economy: An empirical assessment of Brazil. Economic Systems, 41(3), 408-419.
Diebold, F., & Mariano, R. (2012). Comparing predictive accuracy. Journal of Business &
Economics Statistics, 20(1), 134-144.
Flyvbjerg, B. (2011). Over Budget, Over Time, Over and Over Again: Managing Major
Projects. In P. W. G. Morris, J. Pinto, & J. Söderlund (Eds.), The Oxford
Handbook of Project Management (pp. 321-344). Oxford: Oxford University Press.
Fortis, M. F. A., & Gasparini, C. E. (2017). Plurianualidade orçamentária no Brasil:
Diagnóstico, rumos e desafios. Brasília, DF: Enap.
Giacomini, R., Skreta, V., & Turen, J. (2020). Heterogeneity, inattention, and bayesian updates. American Economic Journal: Macroeconomics, 12(1), 282-309.
Guillén, O. T. C., Hecq, A., Issler, J. V., & Saraiva, D. (2015). Forecasting multivariate time
series under present-value model short- and long-run co-movement restrictions. International Journal of Forecasting, 31, 862-875.
Hope, O. (2003). Disclosure Practices, enforcement of accounting standards, and analysts´
forecast accuracy. Journal of Accounting Research, 41(2), 235-272.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy.
International Journal of Forecasting, 22, 679-688.
Jonung, L., & Larch, M. (2006). Improving fiscal policy in the EU: The case for independent
Forecasts. Economic Policy, 21(47), 492-534.
Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32, 669-679.
Lei 200, 1967. Recuperado de http://www.planalto.gov.br/ccivil_03/decreto-lei/del0200.htm
Martinez, A. L. (2007). Determinantes da acurácia das previsões dos analistas do
mercado de capitais. UnB Contábil, 10(2).
Mckenzie, J. (2011). Mean absolute percentage error and bias in economic forecasting.
Economics Letters, 113, 259-262.
Merola, R., & Pérez, J. J. (2013). Fiscal forecast errors: Governments vs independent
agencies? European Journal of Political Economy, 32, 285-299.
Pina, A., & Venes, N. (2011). The political economy of EDP fiscal forecasts: An empirical
assessment. European Journal of Political Economy, 27, 534-546.
Pires, M. C. C. (2006). Credibilidade na política fiscal: Uma análise preliminar para o Brasil.
Revista Economia Aplicada, 10(3), 367-375.
Piza, E. C. (2019). Determinantes dos desvios de execução da política fiscal no Brasil. Revista
de Economia e Agronegócio, 17(2).
Reitano, V., Jones, P., Barrett, N., & Fowles, F. (2019). Forecast bias and capital reserves
accumulation. The Palgrave Handbook of Government Budget Forecasting, 377-396.
Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and
how collective wisdom shapes business, economies, societies, and nations. New York: Anchor Books.
Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction.
New York, NY: Crown Publishing.
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