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.
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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