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Forecasting Using Targeted Diffusion Indexes

Author

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  • Francisco Craveiro Dias
  • Maximiano Pinheiro
  • António Rua

Abstract

The simplicity of the standard diffusion index model of Stock and Watson has certainly contributed to its success among practitioners resulting in a growing body of literature on factor-augmented forecasts. However, as pointed out by Bai and Ng, the ranked factors considered in the forecasting equation depend neither on the variable to be forecasted nor on the forecasting horizon. We propose a refinement of the standard approach that retains the computational simplicity while coping with this limitation. Our approach consists of generating a weighted average of all the principal components, the weights depending both on the eigenvalues of the sample correlation matrix and on the covariance between the estimated factor and the targeted variable at the relevant horizon. This "targeted diffusion index" approach is applied to US data and the results show that it outperforms considerably the standard approach in forecasting several major macroeconomic series. Moreover, the improvement is more significant in the final part of the forecasting evaluation period.

Suggested Citation

  • Francisco Craveiro Dias & Maximiano Pinheiro & António Rua, 2008. "Forecasting Using Targeted Diffusion Indexes," Working Papers w200807, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w200807
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    References listed on IDEAS

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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. 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.
    3. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    4. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    5. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    6. Michael Artis & Anindya Banerjee & Massimiliano Marcellino, "undated". "Factor forecasts for the UK," Working Papers 203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Inoue, Atsushi & Kilian, Lutz, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.
    8. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    9. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
    10. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    11. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    12. 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.
    13. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    14. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    15. Inoue, Atsushi & Kilian, Lutz, 2004. "Bagging Time Series Models," CEPR Discussion Papers 4333, C.E.P.R. Discussion Papers.
    16. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    Citations

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    Cited by:

    1. Paulo Soares Esteves & António Rua, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    2. Sara Serra & José R. Maria, 2008. "Forecasting investment: A fishing contest using survey data," Working Papers w200818, Banco de Portugal, Economics and Research Department.
    3. Francisco Craveiro Dias & Maximiano Pinheiro & António Rua, 2016. "A bottom-up approach for forecasting GDP in a data rich environment," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    4. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    5. Francisco Craveiro Dias & Maximiano Pinheiro & António Rua, 2014. "Forecasting Portuguese GDP with factor models," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
    6. Johannes Tang Kristensen, 2013. "Diffusion Indexes with Sparse Loadings," CREATES Research Papers 2013-22, Department of Economics and Business Economics, Aarhus University.
    7. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    8. Dias, Francisco & Pinheiro, Maximiano & Rua, António, 2015. "Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence," Economic Modelling, Elsevier, vol. 44(C), pages 266-272.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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