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On Event Study Designs and Distributed-Lag Models: Equivalence, Generalization and Practical Implications

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  • Kurt Schmidheiny
  • Sebastian Siegloch

Abstract

We discuss important features and pitfalls of panel-data event study designs. We derive the following main results: First, event study designs and distributed-lag models are numerically identical leading to the same parameter estimates after correct reparametrization. Second, binning of effect window endpoints allows identification of dynamic treatment effects even when no never-treated units are present. Third, classic dummy variable event study designs can be naturally generalized to models that account for multiple events of different sign and intensity of the treatment, which are particularly interesting for research in labor economics and public finance.

Suggested Citation

  • Kurt Schmidheiny & Sebastian Siegloch, 2019. "On Event Study Designs and Distributed-Lag Models: Equivalence, Generalization and Practical Implications," CESifo Working Paper Series 7481, CESifo.
    • Handle: RePEc:ces:ceswps:_7481
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • H00 - Public Economics - - General - - - General
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies

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