IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/29637.html
   My bibliography  Save this paper

Testing for non-causality by using the Autoregressive Metric

Author

Listed:
  • Di Iorio, Francesca
  • Triacca, Umberto

Abstract

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in nite samples. The empirical relevance of the test is illustrated via two applications.

Suggested Citation

  • Di Iorio, Francesca & Triacca, Umberto, 2011. "Testing for non-causality by using the Autoregressive Metric," MPRA Paper 29637, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:29637
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/29637/2/MPRA_paper_29637.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maravall, Agustin & Mathis, Alexandre, 1994. "Encompassing univariate models in multivariate time series : A case study," Journal of Econometrics, Elsevier, vol. 61(2), pages 197-233, April.
    2. Park, Joon Y. & Phillips, Peter C.B., 1989. "Statistical Inference in Regressions with Integrated Processes: Part 2," Econometric Theory, Cambridge University Press, vol. 5(1), pages 95-131, April.
    3. David Giles, 1997. "Causality between the measured and underground economies in New Zealand," Applied Economics Letters, Taylor & Francis Journals, vol. 4(1), pages 63-67.
    4. Neil R. Ericsson & John S. Irons & Ralph W. Tryon, 2001. "Output and inflation in the long run," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 241-253.
    5. Lukasz Lach, 2010. "Application of Bootstrap Methods in Investigation of Size of the Granger Causality Test for Integrated VAR Systems," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 8(2), pages 167-186.
    6. George Mavrotas & Roger Kelly, 2001. "Old Wine in New Bottles: Testing Causality between Savings and Growth," Manchester School, University of Manchester, vol. 69(s1), pages 97-105.
    7. Domenico Piccolo, 1990. "A Distance Measure For Classifying Arima Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 153-164, March.
    8. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    9. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    10. repec:bla:manchs:v:69:y:2001:i:0:p:97-105 is not listed on IDEAS
    11. Coondoo, Dipankor & Dinda, Soumyananda, 2002. "Causality between income and emission: a country group-specific econometric analysis," Ecological Economics, Elsevier, vol. 40(3), pages 351-367, March.
    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. Di Iorio, Francesca & Triacca, Umberto, 2013. "Testing for Granger non-causality using the autoregressive metric," Economic Modelling, Elsevier, vol. 33(C), pages 120-125.
    2. Francesca Di Iorio & Umberto Triacca, 2014. "Testing for A Set of Linear Restrictions in VARMA Models Using Autoregressive Metric: An Application to Granger Causality Test," Econometrics, MDPI, vol. 2(4), pages 1-14, December.
    3. Helmut Lütkepohl, 2013. "Vector autoregressive models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 6, pages 139-164, Edward Elgar Publishing.
    4. Emirmahmutoglu, Furkan & Kose, Nezir, 2011. "Testing for Granger causality in heterogeneous mixed panels," Economic Modelling, Elsevier, vol. 28(3), pages 870-876, May.
    5. Sami Saafi & Meriem Bel Haj Mohamed & Abdeljelil Farhat, 2017. "Untangling the causal relationship between tax burden distribution and economic growth in 23 OECD countries: Fresh evidence from linear and non-linear Granger causality," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 14(2), pages 265-301, December.
    6. Zeynel Abidin Ozdemir, 2010. "Dynamics Of Inflation, Output Growth And Their Uncertainty In The Uk: An Empirical Analysis," Manchester School, University of Manchester, vol. 78(6), pages 511-537, December.
    7. Dufour, Jean-Marie & Pelletier, Denis & Renault, Eric, 2006. "Short run and long run causality in time series: inference," Journal of Econometrics, Elsevier, vol. 132(2), pages 337-362, June.
    8. Entorf, Horst, 1997. "Random walks with drifts: Nonsense regression and spurious fixed-effect estimation," Journal of Econometrics, Elsevier, vol. 80(2), pages 287-296, October.
    9. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    10. Jesus Vazquez, 2002. "Does the Lucas critique apply during hyperinflation?: empirical evidence from four hyperinflationary episodes," Applied Economics, Taylor & Francis Journals, vol. 34(11), pages 1389-1397.
    11. Blangiewicz, Maria & Charemza, Wojciech W., 1999. "East European Economic Reform: Some Simulations on a Structural Vector Autoregressive Model," Journal of Policy Modeling, Elsevier, vol. 21(5), pages 535-557, September.
    12. Dedu, Vasile & Stoica, Tiberiu, 2014. "The Impact of Monetaru Policy on the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 71-86, June.
    13. Baek, Jungho & Cho, Yongsung & Koo, Won W., 2009. "The environmental consequences of globalization: A country-specific time-series analysis," Ecological Economics, Elsevier, vol. 68(8-9), pages 2255-2264, June.
    14. Neeraj, & Panigrahi, Prasanta K., 2017. "Causality and correlations between BSE and NYSE indexes: A Janus faced relationship," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 284-313.
    15. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    16. Guillaume Guex & Sébastien Guex, 2018. "Debt, economic growth, and interest rates: an empirical study of the Swiss case, presenting a new long-term dataset: 1894–2014," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 154(1), pages 1-13, December.
    17. Bauer, Dietmar & Maynard, Alex, 2012. "Persistence-robust surplus-lag Granger causality testing," Journal of Econometrics, Elsevier, vol. 169(2), pages 293-300.
    18. Man-Keun Kim & Kangil Lee, 2015. "Dynamic Interactions between Carbon and Energy Prices in the U.S. Regional Greenhouse Gas Initiative," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 494-501.
    19. Quintos, Carmela E., 1998. "Analysis of cointegration vectors using the GMM approach," Journal of Econometrics, Elsevier, vol. 85(1), pages 155-188, July.
    20. Elliott, Graham & Stock, James H., 1994. "Inference in Time Series Regression When the Order of Integration of a Regressor is Unknown," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 672-700, August.

    More about this item

    Keywords

    AR metric; Bootstrap test; Granger non-causality; VAR;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:pra:mprapa:29637. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.