IDEAS home Printed from https://ideas.repec.org/a/kap/ecopln/v50y2017i3d10.1007_s10644-017-9212-7.html
   My bibliography  Save this article

The return of financial variables in forecasting GDP growth in the G-7

Author

Listed:
  • Petri Kuosmanen

    (University of Vaasa)

  • Juuso Vataja

    (University of Vaasa)

Abstract

The financial crisis and subsequent sovereign debt crisis together had a profound impact on the current economic environment. This study reexamines the established stylized facts and previous evidence regarding the predictive association between financial variables and real economic activity considering changed economic circumstances. This paper focuses on the predictive ability of the term spread, short-term interest rate and stock returns for real GDP growth in the G-7 countries. We compare the predictive content of nominal financial variables with that of real financial variables and consider the proper number of financial predictors and time variations of forecasting performance. The forecasting results unambiguously indicate that financial variables have regained their predictive power since the financial crisis. Moreover, this study shows that real financial variables are superior to nominal variables and that using several financial indicators for forecasting GDP growth is preferable.

Suggested Citation

  • Petri Kuosmanen & Juuso Vataja, 2017. "The return of financial variables in forecasting GDP growth in the G-7," Economic Change and Restructuring, Springer, vol. 50(3), pages 259-277, August.
  • Handle: RePEc:kap:ecopln:v:50:y:2017:i:3:d:10.1007_s10644-017-9212-7
    DOI: 10.1007/s10644-017-9212-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10644-017-9212-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10644-017-9212-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hännikäinen, Jari, 2015. "Zero lower bound, unconventional monetary policy and indicator properties of interest rate spreads," Review of Financial Economics, Elsevier, vol. 26(C), pages 47-54.
    2. Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, vol. 41(7), pages 1375-1401, July.
    3. Jay Choi, Jongmoo & Hauser, Shmuel & Kopecky, Kenneth J., 1999. "Does the stock market predict real activity? Time series evidence from the G-7 countries," Journal of Banking & Finance, Elsevier, vol. 23(12), pages 1771-1792, December.
    4. Mauro, Paolo, 2003. "Stock returns and output growth in emerging and advanced economies," Journal of Development Economics, Elsevier, vol. 71(1), pages 129-153, June.
    5. Michael D. Bordo & Joseph G. Haubrich, 2004. "The Yield Curve, Recessions and the Credibility of the Monetary Regime: Long Run Evidence 1875-1997," NBER Working Papers 10431, National Bureau of Economic Research, Inc.
    6. Michael Dotsey, 1998. "The predictive content of the interest rate term spread for future economic growth," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 31-51.
    7. Ann M. Dombrosky & Joseph G. Haubrich, 1996. "Predicting real growth using the yield curve," Economic Review, Federal Reserve Bank of Cleveland, issue Q I, pages 26-35.
    8. Arturo Estrella & Anthony P. Rodrigues & Sebastian Schich, 2003. "How Stable is the Predictive Power of the Yield Curve? Evidence from Germany and the United States," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 629-644, August.
    9. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    10. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    11. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    12. Raffaella Giacomini & Barbara Rossi, 2006. "How Stable is the Forecasting Performance of the Yield Curve for Output Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 783-795, December.
    13. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    14. Menzie Chinn & Kavan Kucko, 2015. "The Predictive Power of the Yield Curve Across Countries and Time," International Finance, Wiley Blackwell, vol. 18(2), pages 129-156, June.
    15. Md Nain & Bandi Kamaiah, 2014. "Financial development and economic growth in India: some evidence from non-linear causality analysis," Economic Change and Restructuring, Springer, vol. 47(4), pages 299-319, November.
    16. Olivier Jean Blanchard & Stanley Fischer (ed.), 1991. "NBER Macroeconomics Annual 1991," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262521652, December.
    17. 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.
    18. 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.
    19. Ólan T. Henry & Nilss Olekalns & Jonathan Thong, 2004. "Do stock market returns predict changes to output? Evidence from a nonlinear panel data model," Empirical Economics, Springer, vol. 29(3), pages 527-540, September.
    20. Kuosmanen, Petri & Vataja, Juuso, 2014. "Forecasting GDP growth with financial market data in Finland: Revisiting stylized facts in a small open economy during the financial crisis," Review of Financial Economics, Elsevier, vol. 23(2), pages 90-97.
    21. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    22. Michael D. Bordo & Joseph G. Haubrich, 2008. "The Yield Curve as a Predictor of Growth: Long-Run Evidence, 1875-1997," The Review of Economics and Statistics, MIT Press, vol. 90(1), pages 182-185, February.
    23. Binswanger, Mathias, 2000. "Stock market booms and real economic activity: Is this time different?," International Review of Economics & Finance, Elsevier, vol. 9(4), pages 387-415, October.
    24. 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.
    25. Tsouma, Ekaterini, 2009. "Stock returns and economic activity in mature and emerging markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 668-685, May.
    26. Domian, Dale L. & Louton, David A., 1997. "A threshold autoregressive analysis of stock returns and real economic activity," International Review of Economics & Finance, Elsevier, vol. 6(2), pages 167-179.
    27. Kuosmanen, Petri & Vataja, Juuso, 2011. "The role of stock markets vs. the term spread in forecasting macrovariables in Finland," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 124-132, May.
    28. Junttila, Juha & Kinnunen, Heli, 2004. "The performance of economic tracking portfolios in an IT-intensive stock market," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(4), pages 601-623, September.
    29. Kuosmanen, Petri & Nabulsi, Nasib & Vataja, Juuso, 2015. "Financial variables and economic activity in the Nordic countries," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 368-379.
    30. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    31. Binswanger, Mathias, 2004. "Stock returns and real activity in the G-7 countries: did the relationship change during the 1980s?," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(2), pages 237-252, May.
    32. Sharon Kozicki, 1997. "Predicting real growth and inflation with the yield spread," Economic Review, Federal Reserve Bank of Kansas City, vol. 82(Q IV), pages 39-57.
    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. Hajilee, Massomeh & Stringer, Donna Y. & Hayes, Linda A., 2021. "On the link between the shadow economy and stock market development: An asymmetry analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 303-316.
    2. Kuosmanen, Petri & Rahko, Jaana & Vataja, Juuso, 2019. "Predictive ability of financial variables in changing economic circumstances," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 37-47.

    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. Kuosmanen, Petri & Vataja, Juuso, 2019. "Time-varying predictive content of financial variables in forecasting GDP growth in the G-7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 211-222.
    2. Kuosmanen, Petri & Nabulsi, Nasib & Vataja, Juuso, 2015. "Financial variables and economic activity in the Nordic countries," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 368-379.
    3. Kuosmanen, Petri & Rahko, Jaana & Vataja, Juuso, 2019. "Predictive ability of financial variables in changing economic circumstances," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 37-47.
    4. Gebka, Bartosz & Wohar, Mark E., 2018. "The predictive power of the yield spread for future economic expansions: Evidence from a new approach," Economic Modelling, Elsevier, vol. 75(C), pages 181-195.
    5. Evgenidis, Anastasios & Papadamou, Stephanos & Siriopoulos, Costas, 2020. "The yield spread's ability to forecast economic activity: What have we learned after 30 years of studies?," Journal of Business Research, Elsevier, vol. 106(C), pages 221-232.
    6. David C. Wheelock & Mark E. Wohar, 2009. "Can the term spread predict output growth and recessions? a survey of the literature," Review, Federal Reserve Bank of St. Louis, vol. 91(Sep), pages 419-440.
    7. Hännikäinen, Jari, 2017. "When does the yield curve contain predictive power? Evidence from a data-rich environment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1044-1064.
    8. Kuosmanen, Petri & Vataja, Juuso, 2014. "Forecasting GDP growth with financial market data in Finland: Revisiting stylized facts in a small open economy during the financial crisis," Review of Financial Economics, Elsevier, vol. 23(2), pages 90-97.
    9. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    10. David G. McMillan, 2021. "Predicting GDP growth with stock and bond markets: Do they contain different information?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3651-3675, July.
    11. McMillan, David G., 2021. "When and why do stock and bond markets predict US economic growth?," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 331-343.
    12. Joseph G. Haubrich, 2021. "Does the Yield Curve Predict Output?," Annual Review of Financial Economics, Annual Reviews, vol. 13(1), pages 341-362, November.
    13. Petri Kuosmanen & Juuso Vataja, 2014. "Forecasting GDP growth with financial market data in Finland: Revisiting stylized facts in a small open economy during the financial crisis," Review of Financial Economics, John Wiley & Sons, vol. 23(2), pages 90-97, April.
    14. Anna Florio, 2016. "The central bank as shaper and observer of events: The case of the yield spread," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(1), pages 320-346, February.
    15. 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.
    16. B. De Backer & M. Deroose & Ch. Van Nieuwenhuyze, 2019. "Is a recession imminent? The signal of the yield curve," Economic Review, National Bank of Belgium, issue i, pages 69-93, June.
    17. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.
    18. Morell, Joseph, 2018. "The decline in the predictive power of the US term spread: A structural interpretation," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 314-331.
    19. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2012. "The yield curve and the macro-economy across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1950-1970.
    20. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.

    More about this item

    Keywords

    Term spread; Short-term interest rate; Stock market; Forecasting; Macroeconomy;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

    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:kap:ecopln:v:50:y:2017:i:3:d:10.1007_s10644-017-9212-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.