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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.

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File URL: http://www.bcb.gov.br/pec/wps/ingl/wps228.pdf
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Bibliographic Info

Paper provided by Central Bank of Brazil, Research Department in its series Working Papers Series with number 228.

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Date of creation: Dec 2010
Date of revision:
Handle: RePEc:bcb:wpaper:228

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Web page: http://www.bcb.gov.br/?english

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  1. 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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Cited by:
  1. Benjamin M. Tabak & Marcelo Yoshio Takami & J. M. C. Rocha & Daniel O. Cajueiro, 2011. "Directed Clustering Coefficient as a Measure of Systemic Risk in Complex Banking Networks," Working Papers Series 249, Central Bank of Brazil, Research Department.
  2. Eliana González, . "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 643, Banco de la Republica de Colombia.
  3. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," BORRADORES DE ECONOMIA 007996, BANCO DE LA REPÚBLICA.

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