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Forecasting Brazilian Inflation Using a Large Data Set

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  • Francisco Marcos Rodrigues Figueiredo

Abstract

The objective of this paper is to verify if exploiting the large data set available to the Central Bank of Brazil, makes it possible to obtain forecast models that are serious competitors to models typically used by the monetary authorities for forecasting inflation. Some empirical issues such as the optimal number of variables to extract the factors are also addressed. I find that the best performance of the data rich models is usually for 6-step ahead forecasts. Furthermore, the factor model with targeted predictors presents the best results among other data-rich approaches, whereas PLS forecasts show a relative poor performance.

Suggested Citation

  • Francisco Marcos Rodrigues Figueiredo, 2010. "Forecasting Brazilian Inflation Using a Large Data Set," Working Papers Series 228, Central Bank of Brazil, Research Department.
    • Handle: RePEc:bcb:wpaper:228
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    File URL: https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/wps228.pdf
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    References listed on IDEAS

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    1. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
    2. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
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    2. Tabak, Benjamin M. & Takami, Marcelo & Rocha, Jadson M.C. & Cajueiro, Daniel O. & Souza, Sergio R.S., 2014. "Directed clustering coefficient as a measure of systemic risk in complex banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 211-216.
    3. Behnamian, Mehdi & Shojaee, Abdul Nasser & Haji, Gholamali, 2021. "Investigating the Effective Factors in the Growth of Private Sector Investment in Iran," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 7(4), pages 84-57, February.
    4. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 643, Banco de la Republica de Colombia.
    5. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.

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