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Lagged Explanatory Variables and the Estimation of Causal Effects

Author

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  • Bellemare, Marc F.
  • Masaki, Takaaki
  • Pepinsky, Thomas B.

Abstract

Across the social sciences, lagged explanatory variables are a common strategy to confront challenges to causal identification using observational data. We show that "lag identification"--the use of lagged explanatory variables to solve endogeneity problems--is an illusion: lagging independent variables merely moves the channel through which endogeneity biases causal estimates, replacing a "selection on observables" assumption with an equally untestable "no dynamics among unobservables" assumption. We build our argument intuitively using directed acyclic graphs, then provide analytical results on the bias resulting from lag identification in a simple linear regression framework. We then present simulation results that characterize how, even under favorable conditions, lag identification leads to incorrect inferences. These findings have important implications for current practice among applied researchers in political science, economics, and related disciplines. We conclude by specifying the conditions under which lagged explanatory variables are appropriate for identifying causal effects.

Suggested Citation

  • Bellemare, Marc F. & Masaki, Takaaki & Pepinsky, Thomas B., 2015. "Lagged Explanatory Variables and the Estimation of Causal Effects," MPRA Paper 62350, University Library of Munich, Germany, revised 23 Feb 2015.
  • Handle: RePEc:pra:mprapa:62350
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    References listed on IDEAS

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    More about this item

    Keywords

    Causal Identification; Treatment Effects; Lagged Variables;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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