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Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

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  • Carlos, Thiago Carlomagno
  • Marçal, Emerson Fernandes

Abstract

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data

Suggested Citation

  • Carlos, Thiago Carlomagno & Marçal, Emerson Fernandes, 2013. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Textos para discussão 346, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  • Handle: RePEc:fgv:eesptd:346
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    References listed on IDEAS

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    1. Kohn, Robert, 1982. "When is an aggregate of a time series efficiently forecast by its past?," Journal of Econometrics, Elsevier, vol. 18(3), pages 337-349, April.
    2. Joel Bogdanski & Alexandre Antonio Tombini & Sergio R. Da C. Werlang, 2001. "Implementing Inflation Targeting in Brazil," Money Affairs, CEMLA, vol. 0(1), pages 1-23, January-J.
    3. Evans, Martin & Wachtel, Paul, 1993. "Inflation Regimes and the," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(3), pages 475-511, August.
    4. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    5. Bidarkota, Prasad V, 2001. "Alternative Regime Switching Models for Forecasting Inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 21-35, January.
    6. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    9. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    10. Nijman, Theo E & Palm, Franz C, 1990. "Predictive Accuracy Gain from Disaggregate Sampling in ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 405-415, October.
    11. Rose, David E., 1977. "Forecasting aggregates of independent Arima processes," Journal of Econometrics, Elsevier, vol. 5(3), pages 323-345, May.
    12. Ben S. Bernanke, 2007. "Inflation expectations and inflation forecasting," Speech 306, Board of Governors of the Federal Reserve System (U.S.).
    13. Kim, Chang-Jin, 1993. "Unobserved-Component Time Series Models with Markov-Switching Heteroscedasticity: Changes in Regime and the Link between Inflation Rates and Inflation Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 341-349, July.
    14. Arruda, Elano Ferreira & Ferreira, Roberto Tatiwa & Castelar, Ivan, 2011. "Modelos Lineares e Não Lineares da Curva de Phillips para Previsão da Taxa de Inflação no Brasil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 65(3), September.
    15. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    16. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    17. Adolfo Sachsida & Marcio Ribeiro & Claudio Hamilton dos Santos, 2009. "A Curva de Phillips e a Experiência Brasileira," Discussion Papers 1430, Instituto de Pesquisa Econômica Aplicada - IPEA.
    18. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    19. Ard Reijer & Peter Vlaar, 2006. "Forecasting Inflation: An Art as Well as a Science!," De Economist, Springer, vol. 154(1), pages 19-40, March.
    20. Martin Evans & Paul Wachtel, 1993. "Inflation regimes and the sources of inflation uncertainty," Proceedings, Federal Reserve Bank of Cleveland, pages 475-520.
    21. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    22. Palm, Franz C & Nijman, Theo E, 1984. "Missing Observations in the Dynamic Regression Model," Econometrica, Econometric Society, vol. 52(6), pages 1415-1435, November.
    23. Tiao, G. C. & Guttman, Irwin, 1980. "Forecasting contemporal aggregates of multiple time series," Journal of Econometrics, Elsevier, vol. 12(2), pages 219-230, February.
    24. Prasad V. Bidarkota & J. Huston McCulloch, 1998. "Optimal univariate inflation forecasting with symmetric stable shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(6), pages 659-670.
    25. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    26. Areosa, Waldyr Dutra & Medeiros, Marcelo, 2007. "Inflation Dynamics in Brazil: The Case of a Small Open Economy," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 27(1), May.
    27. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    28. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    29. Roma, Moreno & Skudelny, Frauke & Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 374, European Central Bank.
    30. Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.
    31. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
    32. repec:fip:fedgsq:y:2007:i:jul10 is not listed on IDEAS
    33. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    34. Víctor Gómez & Agustín Maravall, 1998. "Automatic Modeling Methods for Univariate Series," Working Papers 9808, Banco de España.
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    4. Olofin, S.O. & Salisu, A.A & Tule, M.K, 2020. "Revised Small Macro-Econometric Model Of The Nigerian Economy," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 20(1), pages 97-116.

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