When do common time series estimands have nonparametric causal meaning?

Fuente: arXiv
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Autori principali: Rambachan, Ashesh, Shephard, Neil
Natura: Preprint
Pubblicazione: 2019
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author Rambachan, Ashesh
Shephard, Neil
author_facet Rambachan, Ashesh
Shephard, Neil
contents In this paper, we introduce the direct potential outcome system as a framework for analyzing dynamic causal effects of assignments on outcomes in observational time series settings. We provide conditions under which common predictive time series estimands, such as the impulse response function, generalized impulse response function, local projection, and local projection instrumental variables, have a nonparametric causal interpretation in terms of dynamic causal effects. The direct potential outcome system therefore provides a foundation for analyzing popular reduced-form methods for estimating the causal effect of macroeconomic shocks on outcomes in time series settings.
format Preprint
id arxiv_https___arxiv_org_abs_1903_01637
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle When do common time series estimands have nonparametric causal meaning?
Rambachan, Ashesh
Shephard, Neil
Econometrics
In this paper, we introduce the direct potential outcome system as a framework for analyzing dynamic causal effects of assignments on outcomes in observational time series settings. We provide conditions under which common predictive time series estimands, such as the impulse response function, generalized impulse response function, local projection, and local projection instrumental variables, have a nonparametric causal interpretation in terms of dynamic causal effects. The direct potential outcome system therefore provides a foundation for analyzing popular reduced-form methods for estimating the causal effect of macroeconomic shocks on outcomes in time series settings.
title When do common time series estimands have nonparametric causal meaning?
topic Econometrics
url https://arxiv.org/abs/1903.01637