IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbwps/20121447.html
   My bibliography  Save this paper

Sometimes it helps: the evolving predictive power of spreads on GDP dynamics

Author

Listed:
  • Nicoletti, Giulio
  • Passaro, Raffaele

Abstract

We investigate the predictive content of credit and government interest spreads with respect to the Italian GDP growth. Our analysis with Dynamic Model Averaging identifies when interest spreads were more useful predictors of economic activity: these periods are not limited to the Great Recession. For credit spreads we gather information from both bank loans and corporate bonds and we compare their predictive role over time and over different forecasting horizons. JEL Classification: C52, E37

Suggested Citation

  • Nicoletti, Giulio & Passaro, Raffaele, 2012. "Sometimes it helps: the evolving predictive power of spreads on GDP dynamics," Working Paper Series 1447, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20121447
    Note: 1874455
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp1447.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gross, Marco, 2011. "Corporate bond spreads and real activity in the euro area - Least Angle Regression forecasting and the probability of the recession," Working Paper Series 1286, European Central Bank.
    2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
    3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    4. Raphael Espinoza & Fabio Fornari & Marco J. Lombardi, 2012. "The Role of Financial Variables in predicting economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(1), pages 15-46, January.
    5. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    6. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    7. Matteo Ciccarelli & Angela Maddaloni & Jose Luis Peydro, 2015. "Trusting the Bankers: A New Look at the Credit Channel of Monetary Policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(4), pages 979-1002, October.
    8. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    9. Jon Faust & Simon Gilchrist & Jonathan H. Wright & Egon Zakrajšsek, 2013. "Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1501-1519, December.
    10. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    11. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    12. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    13. Estrella, Arturo & Hardouvelis, Gikas A, 1991. "The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
    14. Jan Hatzius & Peter Hooper & Frederic S. Mishkin & Kermit L. Schoenholtz & Mark W. Watson, 2010. "Financial Conditions Indexes: A Fresh Look after the Financial Crisis," NBER Working Papers 16150, National Bureau of Economic Research, Inc.
    15. Benati, Luca & Goodhart, Charles, 2008. "Investigating time-variation in the marginal predictive power of the yield spread," Journal of Economic Dynamics and Control, Elsevier, vol. 32(4), pages 1236-1272, April.
    16. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    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. Paulo M.M. Rodrigues & Rita Fradique Lourenço & Robert Hill, 2020. "House price forecasting and uncertainty: Examining Portugal and Spain," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    2. Miguel Belmonte & Gary Koop, 2014. "Model Switching and Model Averaging in Time-Varying Parameter Regression Models," Advances in Econometrics, in: Bayesian Model Comparison, volume 34, pages 45-69, Emerald Group Publishing Limited.
    3. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    4. Le Ha Thu & Roberto Leon-Gonzalez, 2021. "Forecasting Macroeconomic Variables in Emerging Economies: An Application to Vietnam," GRIPS Discussion Papers 21-03, National Graduate Institute for Policy Studies.
    5. Thu, Le Ha & Leon-Gonzalez, Roberto, 2021. "Forecasting macroeconomic variables in emerging economies," Journal of Asian Economics, Elsevier, vol. 77(C).
    6. Babalos, Vassilios & Stavroyiannis, Stavros, 2017. "Modelling correlation dynamics of EMU sovereign debt markets during the recent turmoil," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1021-1029.

    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. 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.
    2. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    3. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    4. Ibarra-Ramírez Raúl, 2021. "The Yield Curve as a Predictor of Economic Activity in Mexico: The Role of the Term Premium," Working Papers 2021-07, Banco de México.
    5. Koop, Gary & Korobilis, Dimitris, 2014. "A new index of financial conditions," European Economic Review, Elsevier, vol. 71(C), pages 101-116.
    6. Bassett, William F. & Chosak, Mary Beth & Driscoll, John C. & Zakrajšek, Egon, 2014. "Changes in bank lending standards and the macroeconomy," Journal of Monetary Economics, Elsevier, vol. 62(C), pages 23-40.
    7. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    8. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    9. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    10. Nathan Bedock & Dalibor Stevanovic, 2017. "An empirical study of credit shock transmission in a small open economy," Canadian Journal of Economics, Canadian Economics Association, vol. 50(2), pages 541-570, May.
    11. Paul Mizen & Veronica Veleanu, 2015. "On the Information Flow from Credit Derivatives to the Macroeconomy," Discussion Papers 2015/21, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    12. Panagiotis Delis & Stavros Degiannakis & George Filis, 2022. "What matters when developing oil price volatility forecasting frameworks?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 361-382, March.
    13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    14. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    15. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
    16. Esteban Prieto & Sandra Eickmeier & Massimiliano Marcellino, 2016. "Time Variation in Macro‐Financial Linkages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1215-1233, November.
    17. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2022. "The Relation between the High-Yield Bond Spread and the Unemployment Rate in the Euro Area," Finance Research Letters, Elsevier, vol. 46(PA).
    18. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    19. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    20. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.

    More about this item

    Keywords

    Bayesian econometrics; GDP forecasting; model averaging;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:ecb:ecbwps:20121447. 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: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/emieude.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.