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A Method for Experimental Events that Break Cointegration: Counterfactual Simulation

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  • 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

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    File URL: https://mpra.ub.uni-muenchen.de/53523/1/MPRA_paper_53523.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. Ole Peters, 2011. "Menger 1934 revisited," Papers 1110.1578, arXiv.org.
    3. Christian Upper, 2007. "Using counterfactual simulations to assess the danger of contagion in interbank markets," BIS Working Papers 234, Bank for International Settlements.
    4. Bell, Peter N, 2014. "Farmland Ownership Restrictions: Between a Rock and a Hard Place," MPRA Paper 53033, University Library of Munich, Germany.
    5. 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.
    6. 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.
    7. 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.
    8. Ole Peters, 2010. "The time resolution of the St. Petersburg paradox," Papers 1011.4404, arXiv.org, revised Mar 2011.
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    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

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