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Cointegration and control: Assessing the impact of events using time series data

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  • Andrew Harvey
  • Stephen Thiele

Abstract

Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that fitting a multivariate time series model offers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as difference in differences and synthetic control, suggesting ways to test these assumptions. Gains from simple and transparent time series models are analysed using examples from the literature, including the California smoking law of 1989 and German reunification. We argue that selecting controls using a time series strategy is preferable to existing data‐driven regression methods.

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  • Andrew Harvey & Stephen Thiele, 2021. "Cointegration and control: Assessing the impact of events using time series data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 71-85, January.
    • Handle: RePEc:wly:japmet:v:36:y:2021:i:1:p:71-85
    DOI: 10.1002/jae.2802
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    Cited by:

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    2. Anderson, Heather M. & Gao, Jiti & Turnip, Guido & Vahid, Farshid & Wei, Wei, 2023. "Estimating the effect of an EU-ETS type scheme in Australia using a synthetic treatment approach," Energy Economics, Elsevier, vol. 125(C).
    3. Kim, Hyejin & Lee, Jungmin, 2019. "Can employment subsidies save jobs? Evidence from a shipbuilding city in South Korea," Labour Economics, Elsevier, vol. 61(C).
    4. Takamitsu Kurita & Bent Nielsen, 2019. "Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms," Econometrics, MDPI, vol. 7(4), pages 1-35, October.
    5. Peter Dreuw, 2023. "Structural time series models and synthetic controls—assessing the impact of the euro adoption," Empirical Economics, Springer, vol. 64(2), pages 681-725, February.
    6. Farid, Moatazbellah, 2020. "The Effect of Brexit on UK Productivity: Synthetic Control Analysis," MPRA Paper 103165, University Library of Munich, Germany.

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