Clustering and External Validity in Randomized Controlled Trials

Deeb, Antoine; Chaisemartin, Clément de

doi:10.2139/ssrn.3630707

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2023

2024

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Cited by 2 publications

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“…The lack of generalizability of estimates obtained from micro data when the population is subject to unaccounted aggregate shocks has also been discussed byRosenzweig and Udry (2020) andDeeb and de Chaisemartin (2022) in the context of RCTs or causal studies with individual-level treatments. Deeb and de Chaisemartin (2022) note that if units are subject to village-level shocks but one clusters at the level of randomization, one can still draw inference on a conditional average treatment effects estimand indexed by the realizations of these shocks.…”

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confidence: 99%

Micro Responses to Macro Shocks

Almuzara,

Sancibrián

2024

Staff Reports (Federal Reserve Bank of New York)

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We study estimation and inference in panel data regression models when the regressors of interest are macro shocks, which speaks to a large empirical literature that targets impulse responses via local projections. Our results hold under general dynamics and are uniformly valid over the degree of signal-to-noise of aggregate shocks. We show that the regression scores feature strong cross-sectional dependence and a known autocorrelation structure induced only by leads of the regressor. In general, including lags as controls and then clustering over the cross-section leads to simple, robust inference.

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confidence: 99%