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A Method for Experimental Events that Break Cointegration: Counterfactual Simulation

  • Bell, Peter N

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.

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File URL: http://mpra.ub.uni-muenchen.de/53523/1/MPRA_paper_53523.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 53523.

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Date of creation: 07 Feb 2014
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Handle: RePEc:pra:mprapa:53523
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  1. Ole Peters, 2011. "Menger 1934 revisited," Papers 1110.1578, arXiv.org.
  2. Allan w. Gregory & Bruce E. Hansen, 1992. "residual-Based Tests for Cointegration in Models with Regime Shifts," Working Papers 862, Queen's University, Department of Economics.
  3. Ole Peters, 2010. "The time resolution of the St. Petersburg paradox," Papers 1011.4404, arXiv.org, revised Mar 2011.
  4. Christian Upper, 2007. "Using counterfactual simulations to assess the danger of contagion in interbank markets," BIS Working Papers 234, Bank for International Settlements.
  5. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  6. Bell, Peter N, 2014. "Farmland Ownership Restrictions: Between a Rock and a Hard Place," MPRA Paper 53033, University Library of Munich, Germany.
  7. 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.
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