Skip to main content

Advertisement

SpringerLink
Log in

The Interdependence Between Commodity-Price and GDP Cycles: A Frequency-Domain Approach

  • Published:
Atlantic Economic Journal Aims and scope Submit manuscript

Abstract

In this paper, the interdependence between aggregate commodity prices and world gross domestic product (GDP) is studied by performing two empirical exercises with long-run data that starts in the nineteenth century. Long−term and medium-term cycles are computed and their degree of synchronization measured for different leads and lags. Causality tests are performed on the frequency domain. Both exercises deepen understanding of these macroeconomic relationships by disentangling them on the time and frequency dimensions, respectively. The results show evidence of cycle synchronization only in the case of super cycles. There is causality evidence from GDP to aggregate commodity prices mostly for long-run frequencies. Therefore, commodity-price trends and super-cycles are demand driven. There is causality evidence between oil-prices and GDP in both causation directions. However, oil-price fluctuations are predictive of GDP for business-cycle frequencies only. Overall, a frequency-domain approach is useful for identifying significant variation in the interdependence between commodity prices and GDP across fluctuation horizons.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. An example of this type of assessments is Morgan Stanley (2015). This article forecast an increase in metal and mineral prices due to the predicted industrial recovery in China. However, its authors warned that this prediction could fail if producer companies did not use enough “supply discipline”.

  2. Diverse studies estimate the cyclical components of business activity using filtering methodologies based on BP filters. Recent examples of these works are Comin and Gertler (2006), Borio (2014) and Drehmann et al. (2012). The BP filter was developed by Baxter and King (1999) and Christiano and Fitzgerald (2003).

  3. Other recent papers have documented the relationship between recent high-growth periods in developing economies and the dynamics of RCP. Collier and Goderis (2012), Garnaut (2012) and Byrne et al. (2013) discuss this relationship. In addition, Baffes and Etienne (2016) as well as Winkelried (2016) provided recent evidence of the Prebisch-Singer hypothesis.

  4. We thank Bilge Erten and Jose A. Ocampo for sharing with us their database on commodity prices.

  5. The synchronization measure proposed by Harding and Pagan (2006) was also tested with qualitatively similar results.

  6. This analysis was also performed with Kendall’s Tau correlations with very similar conclusions.

  7. The non-metal components of the commodity-price index are basically agricultural products and raw materials (Online Supplemental Appendix Table 1).

References

  • Alquist, R., L. Kilian, and R. Vigfusson, (2013). Forecasting the Price of oil, In: Graham, E. and A. Timmermann (Eds), Handbook of economic forecasting, Volume 2A, Elsevier, Amsterdam, pp. 427–508.

    Google Scholar 

  • Alquist, R., Bhattarai, S., & Coibion, O. (2019). Commodity-Price Comovement and global economic activity. Journal of Monetary Economics, In Press. https://doi.org/10.1016/j.jmoneco.2019.02.004.

  • Baffes, J., & Etienne, X. (2016). Analyzing food Price trends in the context of Engel’s law and the Prebisch-singer hypothesis. Oxford Economic Papers, 68(3), 688–713.

    Article  Google Scholar 

  • Baxter, M., & King, R. (1999). Measuring business cycles: Approximate band-pass filters for economic time series. The Review of Economics and Statistics, 81(4), 575–593.

    Article  Google Scholar 

  • Borio, C. (2014). The financial cycle and macroeconomics: What have we learnt? Journal of Banking and Finance, 45, 182–198.

    Article  Google Scholar 

  • Breitung, J., & Candelon, B. (2006). Testing for short and long-run causality: A frequency-domain approach. Journal of Econometrics, 132, 363–378.

    Article  Google Scholar 

  • Breitung, J., & Schreiber, S. (2018). Assessing causality and delay within a frequency band. Econometrics and Statistics, 6(C), 57–73.

    Article  Google Scholar 

  • Byrne, J., Fazio, G., & Fiess, N. (2013). Primary commodity prices: Co-movements, common factors and fundamentals. Journal of Development Economics, 101, 16–26.

    Article  Google Scholar 

  • Cashin, P., & McDermott, J. (2002). The long-run behavior of commodity prices: Small trends and big variability. IMF Staff Papers, 49(2), 175–199.

    Google Scholar 

  • Christiano, L., & Fitzgerald, T. (2003). The band pass filter. International Economic Review, 44(2), 435–465.

    Article  Google Scholar 

  • Collier, P., & Goderis, B. (2012). Commodity prices and growth: An empirical investigation. European Economic Review, 56(6), 1241–1260.

    Article  Google Scholar 

  • Comin, D., & Gertler, M. (2006). Medium-term business cycles. American Economic Review, 96(3), 523–551.

    Article  Google Scholar 

  • Cuddington, J., & Jerrett, D. (2008). Super cycles in real metals prices? IMF Staff Papers, 55(4), 541–565.

    Article  Google Scholar 

  • Dolado, J., & Lütkepohl, H. (1996). Making Wald tests work for Cointegrated VAR systems. Econometric Reviews, 15, 369–386.

    Article  Google Scholar 

  • Doornik, J., & Hansen, H. (2008). An omnibus test for univariate and multivariate normality. Oxford Bulletin of Economics and Statistics, 70, 927–939.

    Article  Google Scholar 

  • Drehmann, M., Borio, C., and Tsatsaronis, K. (2012). Characterising the financial cycle: Don’t lose sight of the medium term! BIS working papers #380, bank for international settlements. http://www.bis.org/publ/work380.pdf

  • Erten, B., & Ocampo, J. A. (2013). Super cycles of commodity prices since the mid-nineteenth century. World Development, 44, 14–30.

    Article  Google Scholar 

  • Garnaut, R. (2012). The contemporary China resources boom. Australian Journal of Agricultural and Resource Economics, 56(2), 222–243.

    Article  Google Scholar 

  • Gil-Alana, L., & Gupta, R. (2014). Persistence and cycles in historical oil-Price data. Energy Economics, 45, 511–516.

    Article  Google Scholar 

  • Grilli, E., & Yang, M. (1988). Primary commodity prices, manufactured goods prices, and the terms of trade of developing countries: What long run shows. The. World Bank Economic Review, 2(1), 1–47.

    Article  Google Scholar 

  • Harding, D., & Pagan, A. (2006). Synchronization of cycles. Journal of Econometrics, 132, 59–79.

    Article  Google Scholar 

  • Harvey, D., Kellard, N., Madsen, J., & Wohar, M. (2017). Long-run commodity prices, economic growth, and interest rates: 17th century to the present day. World Development, 89, 57–70.

    Article  Google Scholar 

  • Hevia, C. (2008). Standard errors using the Delta method and GMM. Available at: http://siteresources.worldbank.org/DEC/Resources/Hevia_se_gmm.pdf

  • International Monetary Fund (2019a). International financial statistics. Available: https://data.imf.org/?sk=4C514D48-B6BA-49ED-8AB9-52B0C1A0179B.

  • International Monetary Fund (2019b). World economic outlook database. World economic and financial surveys. Available: https://www.imf.org/external/pubs/ft/weo/2019/01/weodata/index.aspx.

  • Jacks, D. (2019). From boom to bust: A typology of real commodity prices in the long run. Cliometrica, 13(2), 201–220.

    Article  Google Scholar 

  • Jerrett, D., & Cuddington, J. (2008). Broadening the statistical search for metal Price super cycles to steel and related metals. Resources Policy, 33, 188–195.

    Article  Google Scholar 

  • Lütkepohl, H. (2007). New introduction to multiple time series analysis. Berlin: Springer Verlag, 764 p.

    Google Scholar 

  • Maddison, A. (2004). The world economy: Historical statistics (Vol. 274). OECD: Development Centre Studies.

    Google Scholar 

  • Ocampo, J. A., & Parra, M. (2010). The terms of trade for commodities since the mid-nineteenth century. Journal of Iberian and Latin American Economic History, 28(1), 11–43.

    Article  Google Scholar 

  • Schumpeter, J. (1939). Business cycles. Volumes 1 and 2. New York: McGraw-Hill.

    Google Scholar 

  • Morgan Stanley (2015). Digging up from Rock Bottom. Available at: https://www.morganstanley.com/ideas/metals-mining-digging-up-from-rock-bottom

  • Toda, H., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 98, 225–255.

    Article  Google Scholar 

  • Wei, Y. (2013). Commodity prices, manufactured goods prices and inflation: Evidence from Japan. Economics Bulletin, 33(2), 986–992.

    Google Scholar 

  • Winkelried, D. (2016). Piecewise linear trends and cycles in primary commodity prices. Journal of International Money and Finance, 64, 196–213.

    Article  Google Scholar 

Download references

Acknowledgements

The findings, recommendations, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily reflect the view of the Central Bank of Colombia or its Board of Directors. We are grateful to Luis Fernando Melo, Ana María Fuertes and Lavan Mahadeva, the Editor and anonymous referee for their very useful comments.

Author information

Authors and Affiliations

  1. Banco de la Republica, Cr. 7 No, 14-78, Bogotá, DC, Colombia

    Jair N. Ojeda-Joya & Oscar Jaulin-Mendez

  2. Université Paris-Dauphine, Place du Maréchal de Lattre de Tassigny, 75775, Paris, France

    Juan C. Bustos-Peláez

Authors
  1. Jair N. Ojeda-Joya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar Jaulin-Mendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan C. Bustos-Peláez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jair N. Ojeda-Joya.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 22 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ojeda-Joya, J.N., Jaulin-Mendez, O. & Bustos-Peláez, J.C. The Interdependence Between Commodity-Price and GDP Cycles: A Frequency-Domain Approach. Atl Econ J 47, 275–292 (2019). https://doi.org/10.1007/s11293-019-09635-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11293-019-09635-4

Keywords

JEL

Search

Navigation