IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v31y2016i7p1333-1351.html
   My bibliography  Save this article

Nonlinear Granger Causality: Guidelines for Multivariate Analysis

Author

Listed:
  • Cees Diks
  • Marcin Wolski

Abstract

In this paper we propose an extension of the nonparametric Granger causality test, originally introduced by Diks and Panchenko [2006. A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics \& Control 30, 1647-1669]. We show that the basic test statistics lacks consistency in the multivariate setting. The problem is the result of the kernel density estimator bias, which does not converge to zero at a sufficiently fast rate when the number of conditioning variables is larger than one. In order to overcome this difficulty we apply the data-sharpening method for bias reduction. We then derive the asymptotic properties of the `sharpened' test statistics and we investigate its performance numerically. We conclude with an empirical application to the US grain market.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Cees Diks & Marcin Wolski, 2016. "Nonlinear Granger Causality: Guidelines for Multivariate Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1333-1351, November.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:7:p:1333-1351
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Christopher L. Gilbert, 2010. "How to Understand High Food Prices," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(2), pages 398-425, June.
    2. Sari, Ramazan & Hammoudeh, Shawkat & Chang, Chia-Lin & McAleer, Michael, 2012. "Causality between market liquidity and depth for energy and grains," Energy Economics, Elsevier, vol. 34(5), pages 1683-1692.
    3. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    4. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    5. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    6. Bai, Zhidong & Wong, Wing-Keung & Zhang, Bingzhi, 2010. "Multivariate linear and nonlinear causality tests," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 5-17.
    7. Peter Hall & Michael C. Minnotte, 2002. "High order data sharpening for density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(1), pages 141-157, January.
    8. Popp, Michael P. & Dillon, Carl R. & Keisling, Terry C., 2003. "Economic and weather influences on soybean planting strategies on heavy soils," Agricultural Systems, Elsevier, vol. 76(3), pages 969-984, June.
    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. Ren, Weijie & Li, Baisong & Han, Min, 2020. "A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Wolski, M., 2013. "Exploring Nonlinearities in Financial Systemic Risk," CeNDEF Working Papers 13-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    3. Bonaccolto, G. & Caporin, M. & Gupta, R., 2018. "The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 446-469.
    4. Choudhry, Taufiq & Hassan, Syed S. & Shabi, Sarosh, 2015. "Relationship between gold and stock markets during the global financial crisis: Evidence from nonlinear causality tests," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 247-256.
    5. Palazzi, Rafael Baptista & Figueiredo Pinto, Antonio Carlos & Klotzle, Marcelo Cabus & De Oliveira, Erick Meira, 2020. "Can we still blame index funds for the price movements in the agricultural commodities market?," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 84-93.
    6. Pedro Antonio Martín Cervantes & Nuria Rueda López & Salvador Cruz Rambaud, 2019. "A Causal Analysis of Life Expectancy at Birth. Evidence from Spain," IJERPH, MDPI, vol. 16(13), pages 1-14, July.
    7. Zhan-Ming Chen & Liyuan Wang & Xiao-Bing Zhang & Xinye Zheng, 2019. "The Co-Movement and Asymmetry between Energy and Grain Prices: Evidence from the Crude Oil and Corn Markets," Energies, MDPI, vol. 12(7), pages 1-18, April.
    8. Liow, Kim Hiang & Huang, Yuting, 2018. "The dynamics of volatility connectedness in international real estate investment trusts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 195-210.
    9. Mensi, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong & Yoon, Seong-Min, 2014. "Dynamic spillovers among major energy and cereal commodity prices," Energy Economics, Elsevier, vol. 43(C), pages 225-243.
    10. Francis, Bill B. & Mougoué, Mbodja & Panchenko, Valentyn, 2010. "Is there a symmetric nonlinear causal relationship between large and small firms?," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 23-38, January.
    11. Ahmed Ali & Granberg Mark & Troster Victor & Uddin Gazi Salah, 2022. "Asymmetric dynamics between uncertainty and unemployment flows in the United States," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(1), pages 155-172, February.
    12. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    13. Dutta, Anupam & Bouri, Elie & Roubaud, David, 2019. "Nonlinear relationships amongst the implied volatilities of crude oil and precious metals," Resources Policy, Elsevier, vol. 61(C), pages 473-478.
    14. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," JRFM, MDPI, vol. 11(1), pages 1-29, March.
    15. Henryk Gurgul & Łukasz Lach & Roland Mestel, 2012. "The relationship between budgetary expenditure and economic growth in Poland," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 161-182, March.
    16. Gurgul, Henryk & Lach, Łukasz, 2011. "Causality analysis between public expenditure and economic growth of Polish economy in last decade," MPRA Paper 52281, University Library of Munich, Germany.
    17. Chu, Amanda M.Y. & Lv, Zhihui & Wagner, Niklas F. & Wong, Wing-Keung, 2020. "Linear and nonlinear growth determinants: The case of Mongolia and its connection to China," Emerging Markets Review, Elsevier, vol. 43(C).
    18. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    19. Philip Arestis & Hüseyin Şen & Ayşe Kaya, 2021. "On the linkage between government expenditure and output: empirics of the Keynesian view versus Wagner’s law," Economic Change and Restructuring, Springer, vol. 54(2), pages 265-303, May.
    20. Jinghua Wang & Geoffrey Ngene, 2018. "Symmetric and asymmetric nonlinear causalities between oil prices and the U.S. economic sectors," Review of Quantitative Finance and Accounting, Springer, vol. 51(1), pages 199-218, July.

    More about this item

    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:wly:japmet:v:31:y:2016:i:7:p:1333-1351. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    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.