Forecasting Quarterly Brazilian Gdp Growth Rate With Linear And Nonlinear Diffusion Index Models

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Forecasting Quarterly Brazilian Gdp Growth Rate With Linear And Nonlinear Diffusion Index Models

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Roberto Tatiwa Ferreira
Luiz Ivan de Melo Castelar

Abstract

The present study uses linear and non-linear diffusion index models to produce one-stepahead forecast of quarterly Brazilian GDP growth rate. Diffusion index models are like dynamic factors models. The non-linear diffusion index models used in this work are not only parsimonious ones, but also they try to capture economic cycles using for this goal a Threshold diffusion index model and a Markov-Switching diffusion index model.

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Paper provided by ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics] in its series Anais do XXXIII Encontro Nacional de Economia [Proceedings of the 33th Brazilian Economics Meeting] with number 029.

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Date of creation: 2005
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Handle: RePEc:anp:en2005:029

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Find related papers by JEL classification:
E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

This paper has been announced in the following NEP Reports:

NEP-ALL-2005-12-01 (All new papers)
NEP-ETS-2005-12-01 (Econometric Time Series)
NEP-FOR-2005-12-01 (Forecasting)
NEP-MAC-2005-12-01 (Macroeconomics)

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

Marcelle Chauvet & Elcyon C.R. Lima & Brisne Vasquez, 2002. "Forecasting Brazilian output in the presence of breaks: a comparison of linear and nonlinear models," Working Paper 2002-28, Federal Reserve Bank of Atlanta. [Downloadable!]
Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November. [Downloadable!] (restricted)
Other versions:
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March. [Downloadable!] (restricted)
Other versions:
- Hansen, B.E., 1991. "Inference when a Nuisance Parameter is Not Identified Under the Null Hypothesis," RCER Working Papers 296, University of Rochester - Center for Economic Research (RCER).
Geweke, John F & Singleton, Kenneth J, 1981. "Maximum Likelihood "Confirmatory" Factor Analysis of Economic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 37-54, February. [Downloadable!] (restricted)
Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
Other versions:
- James H. Stock & Mark W. Watson, 1990. "New Indexes of Coincident and Leading Economic Indicators," NBER Reprints 1380, National Bureau of Economic Research, Inc.
Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-91, September. [Downloadable!] (restricted)

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This page was last updated on 2008-9-25.

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