IDEAS home Printed from https://ideas.repec.org/p/ipe/ipetds/1453.html
   My bibliography  Save this paper

Aplicação de um Modelo Fatorial Dinâmico Para Previsão da Arrecadação Tributária no Brasil

Author

Listed:
  • Mário Jorge Mendonça
  • Cláudio Hamilton dos Santos
  • Thiago Guerrera Martins

Abstract

Este artigo tem por objetivo estimar um modelo fatorial dinâmico (MFD) bayesiano para análise e previsão de uma proxy da carga tributária no Brasil mensal no período 1996-2007. Argumenta-se que o emprego desse tipo de modelo é oportuno por permitir o tratamento conjunto do elevado número de tributos que compõem a carga tributária bruta brasileira (CTBB) - simultaneamente levando em consideração as informações contidas nas inter-relações existentes entre esses últimos e permitindo a identificação dos fatores subjacentes às dinâmicas dos mesmos. Além disso, e diferentemente do que é usual na literatura, o componente sazonal das séries é modelado endogenamente, permitindo a obtenção de estimativas melhor ajustadas aos dados e predições mais confiáveis - uma vez que a sazonalidade é uma característica marcante das séries de arrecadação tributária. Por fim, um exercício de projeção para o ano de 2008 é realizado para os 20 impostos que compõem a nossa base de dados The aim of this article is to estimate a Bayesian factorial dynamic model for the analysis and forecasting of the Brazilian tax burden (BTB) using monthly data from 1996 to 2007. Twenty taxes are responsible for about 80% of the BTB, each of which with a distinct seasonal pattern The factorial model has no problems accommodating the high dimensionality of the data-contrarily to what happens, for instance, with VARs-while simultaneously allowing the identification of a short number of factors responsible for the joint dynamics of the various taxes. Therefore, this procedure allows one to obtain relevant insights about the public revenues in Brazil. Moreover, due to the fact that seasonality is a remarkable feature of the series of government receipts, the seasonal component is modeled endogenously using a Fourier form representation that is an unrestricted and flexible way to assess seasonality. Finally, we forecast the future path of the public receipts separately for the period of 2008.

Suggested Citation

  • Mário Jorge Mendonça & Cláudio Hamilton dos Santos & Thiago Guerrera Martins, 2009. "Aplicação de um Modelo Fatorial Dinâmico Para Previsão da Arrecadação Tributária no Brasil," Discussion Papers 1453, Instituto de Pesquisa Econômica Aplicada - IPEA.
    • Handle: RePEc:ipe:ipetds:1453
    as

    Download full text from publisher

    File URL: http://www.ipea.gov.br/portal/images/stories/PDFs/TDs/td_1453.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Geweke, John & Zhou, Guofu, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," The Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 557-587.
    2. Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-357, July.
    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. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    2. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-Based Estimation of Latent Generalized ARCH Structures," Econometrica, Econometric Society, vol. 72(5), pages 1481-1517, September.
    3. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
    4. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
    5. Daniele Bianchi & Massimo Guidolin & Francesco Ravazzolo, 2017. "Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 110-129, January.
    6. Baştürk, N. & Borowska, A. & Grassi, S. & Hoogerheide, L. & van Dijk, H.K., 2019. "Forecast density combinations of dynamic models and data driven portfolio strategies," Journal of Econometrics, Elsevier, vol. 210(1), pages 170-186.
    7. Necati Tekatli, 2007. "Generalized Factor Models: A Bayesian Approach," Working Papers 334, Barcelona School of Economics.
    8. Kim, Hea-Jung & Choi, Taeryon & Jo, Seongil, 2016. "Bayesian factor analysis with uncertain functional constraints about factor loadings," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 110-128.
    9. Tsionas, Mike, 2012. "Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models," MPRA Paper 40966, University Library of Munich, Germany, revised 20 Aug 2012.
    10. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2014. "Bayesian analysis of dynamic factor models: An ex-post approach towards the rotation problem," Kiel Working Papers 1902, Kiel Institute for the World Economy (IfW Kiel).
    11. Eller, Markus & Huber, Florian & Schuberth, Helene, 2020. "How important are global factors for understanding the dynamics of international capital flows?," Journal of International Money and Finance, Elsevier, vol. 109(C).
    12. Philip A. White & Alan E. Gelfand, 2021. "Multivariate functional data modeling with time-varying clustering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 586-602, September.
    13. Crespo Cuaresma, Jesús & Huber, Florian & Onorante, Luca, 2020. "Fragility and the effect of international uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    14. Chaim, Pedro & Laurini, Márcio P., 2019. "Nonlinear dependence in cryptocurrency markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 32-47.
    15. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2023. "General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 69-87, January.
    16. Necati Tekatli, 2010. "A Bayesian Generalized Factor Model with Comparative Analysis (Genellestirilmis Faktor Modellerinin Bayesyen Yaklasimi ve Karsilastirmali Analizi)," Working Papers 1018, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    17. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
    18. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2016. "Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem," Journal of Econometrics, Elsevier, vol. 192(1), pages 190-206.
    19. Chadwick, Meltem, 2010. "Performance of Bayesian Latent Factor Models in Measuring Pricing Errors," MPRA Paper 79060, University Library of Munich, Germany.
    20. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.

    More about this item

    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:ipe:ipetds:1453. 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: Fabio Schiavinatto (email available below). General contact details of provider: https://edirc.repec.org/data/ipeaabr.html .

    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.