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Forecasting inflation through a bottom-up approach: How bottom is bottom?

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  • Duarte, Claudia
  • Rua, Antonio

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Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 24 (2007)
Issue (Month): 6 (November)
Pages: 941-953

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Handle: RePEc:eee:ecmode:v:24:y:2007:i:6:p:941-953

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Web page: http://www.elsevier.com/locate/inca/30411

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References

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  1. Lutkepohl, Helmut, 1984. "Linear transformations of vector ARMA processes," Journal of Econometrics, Elsevier, Elsevier, vol. 26(3), pages 283-293, December.
  2. Ben Bernanke & Jean Boivin & Piotr S. Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, MIT Press, MIT Press, vol. 120(1), pages 387-422, January.
  3. Kwiatkowski, D. & Phillips, P.C.B. & Schmidt, P., 1990. "Testing the Null Hypothesis of Stationarity Against the Alternative of Unit Root : How Sure are we that Economic Time Series have a Unit Root?," Papers, Michigan State - Econometrics and Economic Theory 8905, Michigan State - Econometrics and Economic Theory.
  4. Ard Reijer & Peter Vlaar, 2006. "Forecasting Inflation: An Art as Well as a Science!," De Economist, Springer, Springer, vol. 154(1), pages 19-40, 03.
  5. David Hendry & Michael P. Clements, 2001. "Economic Forecasting: Some Lessons from Recent Research," Economics Papers 2002-W11, Economics Group, Nuffield College, University of Oxford.
  6. Angelini, Elena & Henry, Jérôme & Mestre, Ricardo, 2001. "A multi-country trend indicator for euro area inflation: computation and properties," Working Paper Series, European Central Bank 0060, European Central Bank.
  7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(1), pages 134-44, January.
  8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
  9. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  10. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, Elsevier, vol. 44(1-2), pages 215-238.
  11. Inoue, Atsushi & Kilian, Lutz, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers 3809, C.E.P.R. Discussion Papers.
  12. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  13. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4976, C.E.P.R. Discussion Papers.
  14. Kirstin Hubrich, 2004. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," Computing in Economics and Finance 2004, Society for Computational Economics 230, Society for Computational Economics.
  15. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  16. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers, Oesterreichische Nationalbank (Austrian Central Bank) 73, Oesterreichische Nationalbank (Austrian Central Bank).
  17. Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina & Roma, Moreno & Skudelny, Frauke, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series, European Central Bank 0374, European Central Bank.
  18. Angelini, Elena & Henry, Jérôme & Mestre, Ricardo, 2001. "Diffusion index-based inflation forecasts for the euro area," Working Paper Series, European Central Bank 0061, European Central Bank.
  19. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, Econometric Society, vol. 70(1), pages 191-221, January.
  20. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(2), pages 147-62, April.
  21. J. Joseph Beaulieu & Jeffrey A. Miron, 1992. "Seasonal Unit Roots in Aggregate U.S. Data," NBER Technical Working Papers 0126, National Bureau of Economic Research, Inc.
  22. Granger, Clive W.J. & YOON, GAWON, 2001. "Self-Generating Variables in a Cointegrated VAR Framework," University of California at San Diego, Economics Working Paper Series, Department of Economics, UC San Diego qt6010k0xn, Department of Economics, UC San Diego.
  23. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  24. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 2(3), pages 201-14, July.
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Cited by:
  1. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 14(1), pages C25-C44, February.
  2. Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(4), pages 718-732.
  3. Célérier, C., 2009. "Forecasting inflation in France," Working papers, Banque de France 262, Banque de France.
  4. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2008. "Short-Term Forecasting of GDP Using Large Monthly Datasets: A Pseudo Real-Time Forecast Evaluation Exercise," Bank of Lithuania Working Paper Series, Bank of Lithuania 1, Bank of Lithuania.
  5. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, Springer, vol. 46(2), pages 765-788, March.
  6. Esteves, Paulo Soares, 2013. "Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice," Economic Modelling, Elsevier, Elsevier, vol. 33(C), pages 416-420.
  7. Carlos, Thiago C. & 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, Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
  8. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, Elsevier, vol. 27(3), pages 666-677, May.
  9. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, Elsevier, vol. 29(4), pages 1305-1313.
  10. D'Elia, Enrico, 2010. "Predictions vs preliminary sample estimates," MPRA Paper 36070, University Library of Munich, Germany.

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