IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/53523.html
   My bibliography  Save this paper

A Method for Experimental Events that Break Cointegration: Counterfactual Simulation

Author

Listed:
  • Bell, Peter N

Abstract

In this paper I develop a method to estimate the effect of an event on a time series variable. The event is framed in a quasi-experimental setting with time series observations on a treatment variable, which is affected by the event, and a control variable, which is not. Prior to the event, the two variables are cointegrated. After the event, they are not. Since the event only affects the treatment variable, the method uses observations on the control variable after the event and the distribution of difference in differences before the event to simulate values for the treatment variable as-if the event did not occur; hence the name counterfactual simulation. I describe theoretical properties of the method and show the method in action with purpose-built data.

Suggested Citation

  • Bell, Peter N, 2014. "A Method for Experimental Events that Break Cointegration: Counterfactual Simulation," MPRA Paper 53523, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:53523
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/53523/1/MPRA_paper_53523.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Upper, 2007. "Using counterfactual simulations to assess the danger of contagion in interbank markets," BIS Working Papers 234, Bank for International Settlements.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    3. Gregory, Allan W. & Hansen, Bruce E., 1996. "Residual-based tests for cointegration in models with regime shifts," Journal of Econometrics, Elsevier, vol. 70(1), pages 99-126, January.
    4. Ole Peters, 2010. "The time resolution of the St. Petersburg paradox," Papers 1011.4404, arXiv.org, revised Mar 2011.
    5. Deirdre N. McCloskey & Stephen T. Ziliak, 1996. "The Standard Error of Regressions," Journal of Economic Literature, American Economic Association, vol. 34(1), pages 97-114, March.
    6. Ole Peters, 2011. "Menger 1934 revisited," Papers 1110.1578, arXiv.org.
    7. Bell, Peter N, 2014. "Farmland Ownership Restrictions: Between a Rock and a Hard Place," MPRA Paper 53033, University Library of Munich, Germany.
    8. Gregory, Allan W & Hansen, Bruce E, 1996. "Tests for Cointegration in Models with Regime and Trend Shifts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(3), pages 555-560, August.
    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. Francesca Iorio & Stefano Fachin, 2014. "Savings and investments in the OECD: a panel cointegration study with a new bootstrap test," Empirical Economics, Springer, vol. 46(4), pages 1271-1300, June.
    2. Roberto Cellini & Tiziana Cuccia, 2013. "Museum and monument attendance and tourism flow: a time series analysis approach," Applied Economics, Taylor & Francis Journals, vol. 45(24), pages 3473-3482, August.
    3. Kanjilal, Kakali & Ghosh, Sajal, 2013. "Environmental Kuznet’s curve for India: Evidence from tests for cointegration with unknown structuralbreaks," Energy Policy, Elsevier, vol. 56(C), pages 509-515.
    4. Tarlok Singh, 2016. "On the sectoral linkages and pattern of economic growth in India," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 21(2), pages 257-275, April.
    5. Rosa, Franco & Vasciaveo, Michela & Weaver, Robert D., 2014. "Agricultural and oil commodities: price transmission and market integration between US and Italy," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 3(2), pages 1-25, August.
    6. Kühl, Michael, 2007. "Cointegration in the foreign exchange market and market efficiency since the introduction of the Euro: Evidence based on bivariate cointegration analyses," University of Göttingen Working Papers in Economics 68, University of Goettingen, Department of Economics.
    7. Özgür Özaydın* & H. Alper Güzel, 2019. "Oil Consumption and Economic Growth in Turkey: An ARDL Bounds Test Approach in the Presence of Structural Breaks," Business, Management and Economics Research, Academic Research Publishing Group, vol. 5(6), pages 77-85, 06-2019.
    8. Waheed A. Banafea, 2014. "Structural Breaks and Causality Relationship between Economic Growth and Energy Consumption in Saudi Arabia," International Journal of Energy Economics and Policy, Econjournals, vol. 4(4), pages 726-734.
    9. Campos, Julia & Ericsson, Neil R. & Hendry, David F., 1996. "Cointegration tests in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 70(1), pages 187-220, January.
    10. Muñoz, M. Pilar & Dickey, David A., 2009. "Are electricity prices affected by the US dollar to Euro exchange rate? The Spanish case," Energy Economics, Elsevier, vol. 31(6), pages 857-866, November.
    11. Nooman Rebei & Rashid Sbia, 2021. "Transitory and permanent shocks in the global market for crude oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 1047-1064, November.
    12. Cristian Barra & Giovanna Bimonte & Pietro Spennati, 2015. "Did fiscal institutions affect Wagner's law in Italy during 1951-2009 period? An empirical analysis," Applied Economics, Taylor & Francis Journals, vol. 47(59), pages 6409-6424, December.
    13. Voronkova, Svitlana, 2004. "Equity market integration in Central European emerging markets: A cointegration analysis with shifting regimes," International Review of Financial Analysis, Elsevier, vol. 13(5), pages 633-647.
    14. Magazzino, Cosimo & Drago, Carlo & Schneider, Nicolas, 2023. "Evidence of supply security and sustainability challenges in Nigeria’s power sector," Utilities Policy, Elsevier, vol. 82(C).
    15. Nidhaleddine Ben Cheikh & Christophe Rault, 2016. "Recent estimates of exchange rate pass-through to import prices in the euro area," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 152(1), pages 69-105, February.
    16. Tang, Chor Foon, 2011. "Tourism, real output and real effective exchange rate in Malaysia: a view from rolling sub-samples," MPRA Paper 29379, University Library of Munich, Germany.
    17. Wu, Jyh-lin, 1998. "Are budget deficits "too large"?: The evidence from Taiwan," Journal of Asian Economics, Elsevier, vol. 9(3), pages 519-528.
    18. Holmes, Mark J., 2011. "Threshold cointegration and the short-run dynamics of twin deficit behaviour," Research in Economics, Elsevier, vol. 65(3), pages 271-277, September.
    19. Li, Sisi & Khan, Sufyan Ullah & Yao, Yao & Chen, George S. & Zhang, Lin & Salim, Ruhul & Huo, Jiaying, 2022. "Estimating the long-run crude oil demand function of China: Some new evidence and policy options," Energy Policy, Elsevier, vol. 170(C).
    20. Chiappini, Raphaël & Jégourel, Yves & Raymond, Paul, 2019. "Towards a worldwide integrated market? New evidence on the dynamics of U.S., European and Asian natural gas prices," Energy Economics, Elsevier, vol. 81(C), pages 545-565.

    More about this item

    Keywords

    Quasi-experiment; cointegration; time series; counterfactual; simulation;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

    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:53523. 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.