IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v27y2008i2p95-108.html
   My bibliography  Save this article

Linking series generated at different frequencies This work is part of a PhD dissertation presented at the University of California, San Diego (1999)

Author

Listed:
  • Namwon Hyung

    (Department of Economics, University of Seoul, Seoul, Korea)

  • Clive W.J. Granger

    (Department of Economics, University of California, San Diego, California, USA)

Abstract

This is a report on our studies of the systematical use of mixed-frequency datasets. We suggest that the use of high-frequency data in forecasting economic aggregates can increase the accuracy of forecasts. The best way of using this information is to build a single model that relates the data of all frequencies, for example, an ARMA model with missing observations. As an application of linking series generated at different frequencies, we show that the use of a monthly industrial production index improves the predictability of the quarterly GNP. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Namwon Hyung & Clive W.J. Granger, 2008. "Linking series generated at different frequencies This work is part of a PhD dissertation presented at the University of California, San Diego (1999)," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 95-108.
    • Handle: RePEc:jof:jforec:v:27:y:2008:i:2:p:95-108
    DOI: 10.1002/for.1042
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/for.1042
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.1042?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
    2. 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.
    3. Lutkepohl, Helmut, 1984. "Linear aggregation of vector autoregressive moving average processes," Economics Letters, Elsevier, vol. 14(4), pages 345-350.
    4. Daniel M. Chin & Preston J. Miller, 1996. "Using monthly data to improve quarterly model forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 20(Spr), pages 16-33.
    5. Liu, H & Hall, Stephen G, 2001. "Creating High-Frequency National Accounts with State-Space Modelling: A Monte Carlo Experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 441-449, September.
    6. C. W. J. Granger, 1998. "Extracting information from mega‐panels and high‐frequency data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 52(3), pages 258-272, November.
    7. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    8. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    9. 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.
    10. Abeysinghe, Tilak, 1998. "Forecasting Singapore's quarterly GDP with monthly external trade," International Journal of Forecasting, Elsevier, vol. 14(4), pages 505-513, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
    2. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    3. Pedregal, Diego J. & Pérez, Javier J., 2010. "Should quarterly government finance statistics be used for fiscal surveillance in Europe?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 794-807, October.
    4. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Dandan & Jansen, Dennis W., 2007. "Macroeconomic forecasting using structural factor analysis," International Journal of Forecasting, Elsevier, vol. 23(4), pages 655-677.
    2. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    3. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    4. Curran, Declan & Funke, Michael, 2006. "Taking the temperature: forecasting GDP growth for mainland in China," BOFIT Discussion Papers 6/2006, Bank of Finland Institute for Emerging Economies (BOFIT).
    5. repec:zbw:bofitp:2006_006 is not listed on IDEAS
    6. Luis C. Nunes, 2005. "Nowcasting quarterly GDP growth in a monthly coincident indicator model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 575-592.
    7. Declan Curran & Michael Funke, 2006. "Taking the Temperature - Forecasting GDP Growth for Mainland China," Quantitative Macroeconomics Working Papers 20606, Hamburg University, Department of Economics.
    8. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    9. Bentes, Sonia R. & Menezes, Rui, 2013. "On the predictability of realized volatility using feasible GLS," Journal of Asian Economics, Elsevier, vol. 28(C), pages 58-66.
    10. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.
    11. Franck Sédillot, 2001. "La pente des taux contient-elle de l'information sur l'activité économique future ?," Economie & Prévision, La Documentation Française, vol. 147(1), pages 141-157.
    12. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    13. Boucher, Christophe & Maillet, Bertrand & Michel, Thierry, 2008. "Do misalignments predict aggregated stock-market volatility?," Economics Letters, Elsevier, vol. 100(2), pages 317-320, August.
    14. de Albuquerquemello, Vinícius Phillipe & de Medeiros, Rennan Kertlly & da Nóbrega Besarria, Cássio & Maia, Sinézio Fernandes, 2018. "Forecasting crude oil price: Does exist an optimal econometric model?," Energy, Elsevier, vol. 155(C), pages 578-591.
    15. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    16. Costantini, Mauro & Pappalardo, Carmine, 2010. "A hierarchical procedure for the combination of forecasts," International Journal of Forecasting, Elsevier, vol. 26(4), pages 725-743, October.
    17. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    18. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    19. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    20. ALBULESCU, Claudiu Tiberiu & Pepin, Dominique, 2018. "Monetary Integration, Money-Demand Stability, and the Role of Monetary Overhang in Forecasting Inflation in CEE Countries," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 33(4), pages 841-879.
    21. Mauro Costantini & Ulrich Gunter & Robert M. Kunst, 2017. "Forecast Combinations in a DSGE‐VAR Lab," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(3), pages 305-324, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:27:y:2008:i:2:p:95-108. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.