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Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)

Author

Listed:
  • Duo Qin

    (Queen Mary, University of London)

  • Marie Anne Cagas

    (Asian Development Bank (ADB), and University of the Philippines)

  • Geoffrey Ducanes

    (Asian Development Bank (ADB), and University of the Philippines)

  • Nedelyn Magtibay-Ramos

    (Asian Development Bank (ADB))

  • Pilipinas Quising

    (Asian Development Bank (ADB))

Abstract

This paper compares forecast performance of the ALI method and the MESMs and seeks ways of improving the ALI method. Inflation and GDP growth form the forecast objects for comparison, using data from China, Indonesia and the Philippines. The ALI method is found to produce better forecasts than those by MESMs in general, but the method is found to involve greater uncertainty in choosing indicators, mixing data frequencies and utilizing unrestricted VARs. Two possible improvements are found helpful to reduce the uncertainty: (i) give theory priority in choosing indicators and include theory-based disequilibrium shocks in the indicator sets; and (ii) reduce the VARs by means of the general→specific model reduction procedure.

Suggested Citation

  • Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2006. "Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)," Working Papers 554, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:554
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    References listed on IDEAS

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    1. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin R. Weale, 2001. "An automatic leading indicator of economic activity: forecasting GDP growth for European countries," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-37.
    2. Claude Diebolt & Catherine Kyrtsou, 2005. "New Trends in Macroeconomics," Post-Print hal-00279607, HAL.
    3. Claude Diebolt & Michael Haupert, 2018. "Cliometrics," Working Papers of BETA 2018-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    4. George Kapetanios, 2002. "Factor Analysis Using Subspace Factor Models: Some Theoretical Results and an Application to UK Inflation Forecasting," Working Papers 466, Queen Mary University of London, School of Economics and Finance.
    5. Ben D. MacArthur & Richard O. C. Oreffo, 2005. "Bridging the gap," Nature, Nature, vol. 433(7021), pages 19-19, January.
    6. Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Qin, Duo & Quising, Pilipinas, 2006. "A small macroeconometric model of the Philippine economy," Economic Modelling, Elsevier, vol. 23(1), pages 45-55, January.
    7. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1319-1347, October.
    8. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    9. David F. Hendry, 2005. "Bridging the Gap: Linking Economics and Econometrics," Springer Books, in: Claude Diebolt & Catherine Kyrtsou (ed.), New Trends in Macroeconomics, pages 53-77, Springer.
    10. Lippi, Marco & Reichlin, Lucrezia & Forni, Mario, 2003. "Opening the Black Box: Structural Factor Models versus Structural VARs," CEPR Discussion Papers 4133, C.E.P.R. Discussion Papers.
    11. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    12. George Kapetanios, 2004. "A New Method for Determining the Number of Factors in Factor Models with Large Datasets," Working Papers 525, Queen Mary University of London, School of Economics and Finance.
    13. George Kapetanios, 2004. "A New Method for Determining the Number of Factors in Factor Models with Large Datasets," Working Papers 525, Queen Mary University of London, School of Economics and Finance.
    14. 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.
    15. Claude Diebolt & Catherine Kyrtsou (ed.), 2005. "New Trends in Macroeconomics," Springer Books, Springer, number 978-3-540-28556-4, January.
    16. Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, June.
    17. David F. Hendry, 2004. "Unpredictability and the Foundations of Economic Forecasting," Economics Papers 2004-W15, Economics Group, Nuffield College, University of Oxford.
    18. George Kapetanios, 2002. "Factor Analysis Using Subspace Factor Models: Some Theoretical Results and an Application to UK Inflation Forecasting," Working Papers 466, Queen Mary University of London, School of Economics and Finance.
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    1. Aminullah, Erman, 2024. "Forecasting of technology innovation and economic growth in Indonesia," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

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    More about this item

    Keywords

    Dynamic factor models; Model reduction; VAR;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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