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

  • Mário Jorge Mendonça
  • Cláudio Hamilton dos Santos
  • Thiago Guerrera Martins
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    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.

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    Paper provided by Instituto de Pesquisa Econômica Aplicada - IPEA in its series Discussion Papers with number 1453.

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    Length: 36 pages
    Date of creation: Dec 2009
    Date of revision:
    Handle: RePEc:ipe:ipetds:1453
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    1. John Geweke & Guofu Zhou, 1995. "Measuring the pricing error of the arbitrage pricing theory," Staff Report 189, Federal Reserve Bank of Minneapolis.
    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 338-57, July.
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