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When do common time series estimands have nonparametric causal meaning?

Ashesh Rambachan, Neil Shephard

arXiv 5 Mar 2019 · Econometrics · 12 citations (OpenAlex)

arXiv:1903.01637 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Stock, J. H. and M. W. Watson (2018) Identification and estimation of dynamic causal effects in macroeconomics using external instruments1.000105100%
2Jordá, Óscar., M. Schularick, and A. M. Taylor (2020) The effects of quasi-random monetary experiments1.00085100%
3Plagborg-Mller, M. and C. K. Wolf (2022) Instrumental variable identification of dynamic variance decompositions1.00064100%
4Sims, C. A (1980) Macroeconomics and reality1.00064100%
5Jordá, Óscar., M. Schularick, and A. M. Taylor (2015) Betting the house1.00063100%
6Ramey, V. A. and S. Zubairy (2018) Government spending multipliers in good times and in bad: Evidence from US historical data1.00054100%
7Gertler, M. L. and P. Karadi (2015) Monetary policy surprises, credit costs, and economic activity1.00053100%
8Ramey, V. A (2016) Macroeconomics shocks and their propagation0.92843100%
9Jordá, Óscar (2005) Estimation and inference of impulse responses by local projections0.84333100%
10Nakamura, E. and J. Steinsson (2018) Identification in macroeconomics0.84333100%

Showing the top 10 of 86 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly1.00094
2Control VAR: a counterfactual based approach to inference in macroeconomics0.950146
3The causal interpretation of panel vector autoregressions0.92855
4When and Why State-Dependent Local Projections Work0.92843
5When are time series predictions causal? The potential system and dynamic causal effects0.92844
6Design-Based Inference for Time-Series GMM0.92843
7Semiparametric inference for impulse response functions using double/debiased machine learning0.81142
8Dynamic Local Average Treatment Effects in Time Series0.69351
9Nonlinearity in Dynamic Causal Effects: Making the Bad into the Good, and the Good into the Great?0.64422
10Identification, estimation and inference in Panel Vector Autoregressions using external instruments0.64422