A Method for Experimental Events that Break Cointegration: Counterfactual Simulation
AbstractIn 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 53523.
Date of creation: 07 Feb 2014
Date of revision:
Quasi-experiment; cointegration; time series; counterfactual; simulation;
Find related papers by 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
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-02-15 (All new papers)
- NEP-ECM-2014-02-15 (Econometrics)
- NEP-ETS-2014-02-15 (Econometric Time Series)
- NEP-SOG-2014-02-15 (Sociology of Economics)
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