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Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function

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  • Ashesh Rambachan
  • Neil Shephard

Abstract

Bojinov & Shephard (2019) defined potential outcome time series to nonparametrically measure dynamic causal effects in time series experiments. Four innovations are developed in this paper: "instrumental paths," treatments which are "shocks," "linear potential outcomes" and the "causal response function." Potential outcome time series are then used to provide a nonparametric causal interpretation of impulse response functions, generalized impulse response functions, local projections and LP-IV.

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  • Ashesh Rambachan & Neil Shephard, 2019. "Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function," Papers 1903.01637, arXiv.org, revised Feb 2020.
    • Handle: RePEc:arx:papers:1903.01637
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    Cited by:

    1. Hiroyuki Kawakatsu, 2022. "Local projection variance impulse response," Empirical Economics, Springer, vol. 62(3), pages 1219-1244, March.
    2. Matthias R. Fengler & Jeannine Polivka, 2024. "Structural Volatility Impulse Response Analysis," Swiss Finance Institute Research Paper Series 24-63, Swiss Finance Institute.
    3. Fengler, Matthias & Polivka, Jeannine, 2022. "Structural Volatility Impulse Response Analysis," Economics Working Paper Series 2211, University of St. Gallen, School of Economics and Political Science, revised Nov 2022.
    4. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    5. Iavor Bojinov & Ashesh Rambachan & Neil Shephard, 2021. "Panel experiments and dynamic causal effects: A finite population perspective," Quantitative Economics, Econometric Society, vol. 12(4), pages 1171-1196, November.

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