IDEAS home Printed from https://ideas.repec.org/p/tcb/econot/2018.html
   My bibliography  Save this paper

Weekly Economic Conditions Index for Turkey

Author

Listed:
  • Aysu Celgin
  • Mahmut Gunay

Abstract

[EN] In this study, a weekly index that aims tracking developments in economic activity in a timely manner is introduced. The index is formed by using weekly annual percentage changes of credit growth, expenditures by domestic and foreign cards, total number of job postings, electricity consumption and foreign trade data. Index is used to analyze the effects of the coronavirus pandemic on the economic activity. The index indicates that the pandemic started to affect the economic activity negatively in the second half of March and the economy started to recover starting from the first week of May as the restrictions are started to be eased gradually. Overall, the index is successful in tracking the economic activity in Turkey. As a result, the index, which can be updated on a weekly frequency with the flow of information, can be used to produce timely nowcasts for the GDP growth. [TR] Bu calismada, gunluk ve haftalik olarak aciklanan yuksek frekansli veriler kullanilarak iktisadi faaliyetteki gelismeleri zamanli olarak izlemeye imkan veren haftalik frekansta olusturulmus bir endeks tanitilmaktadir. Krediler, banka ve kredi kartiyla yapilan harcamalar, is ilanlari, elektrik tuketimi ve dis ticaret verilerinin haftalik frekansta yillik yuzde degisimlerinden olusturulan endeks yardimiyla koronavirus pandemisinin iktisadi faaliyet uzerine etkileri analiz edilmistir.Endeks, pandemiye bagli etkilerin Mart ayinin ikinci yarisindan itibaren iktisadi faaliyeti olumsuz etkiledigine,Mayis ayi ile birlikte ise dipten donus sinyallerinin basladigina isaret etmektedir. Endeksin milli gelir buyumesini takip etmekte faydali oldugu bulunmustur.Bu cercevede, veri akisi ile birlikte haftalik olarak guncellenebilen endeks kisa donemli milli gelir tahminlerinde kullanilabilecektir.

Suggested Citation

  • Aysu Celgin & Mahmut Gunay, 2020. "Weekly Economic Conditions Index for Turkey," CBT Research Notes in Economics 2018, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
  • Handle: RePEc:tcb:econot:2018
    as

    Download full text from publisher

    File URL: https://www.tcmb.gov.tr/wps/wcm/connect/e35e43c4-45d6-4a89-88bf-2959cb524dbd/en2018eng.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-e35e43c4-45d6-4a89-88bf-2959cb524dbd-nnADSFk
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    2. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    3. 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.
    4. 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.
    5. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    6. Caglar Yunculer, 2015. "Estimating the Bridging Day Effect on Turkish Industrial Production," CBT Research Notes in Economics 1515, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    7. Adrjan, Pawel & Lydon, Reamonn, 2020. "Covid-19 and the global labour market: Impact on job postings," Economic Letters 03/EL/20, Central Bank of Ireland.
    8. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    9. Daniel Lewis & Karel Mertens & James H. Stock, 2020. "U.S. Economic Activity During the Early Weeks of the SARS-Cov-2 Outbreak," NBER Working Papers 26954, National Bureau of Economic Research, Inc.
    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. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.

    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. Simon Freyaldenhoven, 2020. "Identification Through Sparsity in Factor Models," Working Papers 20-25, Federal Reserve Bank of Philadelphia.
    2. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
    3. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    4. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    5. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.
    6. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Daniel A. Dias & João B. Duarte, 2019. "Monetary policy, housing rents, and inflation dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 673-687, August.
    8. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    9. Boriss Siliverstovs & Daniel Wochner, 2019. "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," KOF Working papers 19-463, KOF Swiss Economic Institute, ETH Zurich.
    10. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
    11. Forni, Mario & Gambetti, Luca & Lippi, Marco & Sala, Luca, 2020. "Common Component Structural VARs," CEPR Discussion Papers 15529, C.E.P.R. Discussion Papers.
    12. Cubadda, Gianluca & Guardabascio, Barbara, 2019. "Representation, estimation and forecasting of the multivariate index-augmented autoregressive model," International Journal of Forecasting, Elsevier, vol. 35(1), pages 67-79.
    13. Paccagnini, Alessia, 2019. "Did financial factors matter during the Great Recession?," Economics Letters, Elsevier, vol. 174(C), pages 26-30.
    14. Rueben Ellul & Germano Ruisi, 2022. "Nowcasting the Maltese economy with a dynamic factor model," CBM Working Papers WP/02/2022, Central Bank of Malta.
    15. Philipp Gersing & Christoph Rust & Manfred Deistler, 2023. "Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Jan 2024.
    16. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    17. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    18. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    19. Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
    20. Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2018. "Dynamic factor model with infinite‐dimensional factor space: Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 625-642, August.

    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:2018. 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.