IDEAS home Printed from https://ideas.repec.org/p/tcb/econot/1622.html
   My bibliography  Save this paper

Forecasting Turkish GDP Growth : Bottom-Up vs Direct?

Author

Listed:
  • Mahmut Gunay

Abstract

[EN] In this note, we compare performance of direct and bottom-up approaches to forecasting Turkish GDP growth. In the bottom-up approach, we forecast each component separately and then aggregate these forecasts to reach GDP growth forecast. In the direct approach, we model and forecast GDP growth itself. Results indicate that bottom-up approach helps reduce forecast errors. Importance of the bottom-up approach becomes more evident when we take into account the storytelling dimension of forecasting. [TR] Bu calismada, milli gelir tahmini icin dogrudan ve dolayli yaklasimlarin performanslari karsilastirilmaktadir. Dolayli yaklasimda milli gelirin alt kalemleri ayri ayri tahmin edilip, bu tahminlerin birlestirilmesiyle milli gelir buyume tahmini olusturulmaktadir. Dogrudan yaklasimda ise milli gelir buyumesinin kendisi modellenmekte ve tahmin edilmektedir. Sonuclar, dolayli yaklasimin tahmin hatalarini azalttigini gostermektedir. Tahminlerin salt rakam sunmaktan ziyade iktisadi bir oyku anlatmak icin de kullanildigi dikkate alindiginda, daha kapsamli analiz yapmaya imkan veren dolayli yaklasimin onemi belirginlesmektedir.

Suggested Citation

  • Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth : Bottom-Up vs Direct?," CBT Research Notes in Economics 1622, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    • Handle: RePEc:tcb:econot:1622
    as

    Download full text from publisher

    File URL: https://www.tcmb.gov.tr/wps/wcm/connect/18a704c7-3f49-4f39-a710-50620508fef3/en1622eng.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-18a704c7-3f49-4f39-a710-50620508fef3-m3fw5s.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
    2. António Rua & Paulo Esteves, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    3. repec:zbw:iwhdps:7-13 is not listed on IDEAS
    4. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    5. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    6. Hahn, Elke & Skudelny, Frauke, 2008. "Early estimates of euro area real GDP growth: a bottom up approach from the production side," Working Paper Series 975, European Central Bank.
    Full references (including those not matched with items on IDEAS)

    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. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    2. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    3. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    4. Hanslin Grossmann, Sandra & Scheufele, Rolf, 2015. "Foreign PMIs: A reliable indicator for Swiss exports," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112830, Verein für Socialpolitik / German Economic Association.
    5. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    6. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    7. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    8. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    9. Dées, Stéphane & Güntner, Jochen, 2014. "Analysing and forecasting price dynamics across euro area countries and sectors: a panel VAR approach," Working Paper Series 1724, European Central Bank.
    10. Marcus P. A. Cobb, 2020. "Aggregate density forecasting from disaggregate components using Bayesian VARs," Empirical Economics, Springer, vol. 58(1), pages 287-312, January.
    11. Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    12. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    13. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
    14. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    15. Cobb, Marcus P A, 2017. "Aggregate Density Forecasting from Disaggregate Components Using Large VARs," MPRA Paper 76849, University Library of Munich, Germany.
    16. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.
    17. Marcus Cobb, 2014. "GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework," Working Papers Central Bank of Chile 721, Central Bank of Chile.
    18. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    19. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    20. Douglas Sutherland & Peter Hoeller & Balázs Égert & Oliver Röhn, 2010. "Counter-cyclical Economic Policy," OECD Economics Department Working Papers 760, OECD Publishing.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:tcb:econot:1622. 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: the person in charge or the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/tcmgvtr.html .

    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.