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Asymptotic theory and wild bootstrap inference with clustered errors. / Djogbenou, Antoine A.; MacKinnon, James G.; Nielsen, Morten Ørregaard.
In: Journal of Econometrics, Vol. 212, No. 2, 10.2019, p. 393-412.Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
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TY - JOUR
T1 - Asymptotic theory and wild bootstrap inference with clustered errors
AU - Djogbenou, Antoine A.
AU - MacKinnon, James G.
AU - Nielsen, Morten Ørregaard
PY - 2019/10
Y1 - 2019/10
N2 - We study inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap. We state conditions under which asymptotic and bootstrap tests and confidence intervals are asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. We also derive Edgeworth expansions for the asymptotic and bootstrap test statistics. Simulation experiments illustrate the theoretical results and suggest that alternative variants of the wild cluster bootstrap may perform quite differently. The Edgeworth expansions explain the overrejection of asymptotic tests and shed light on the choice of auxiliary distribution and whether to use restricted or unrestricted estimates in the bootstrap data-generating process.
AB - We study inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap. We state conditions under which asymptotic and bootstrap tests and confidence intervals are asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. We also derive Edgeworth expansions for the asymptotic and bootstrap test statistics. Simulation experiments illustrate the theoretical results and suggest that alternative variants of the wild cluster bootstrap may perform quite differently. The Edgeworth expansions explain the overrejection of asymptotic tests and shed light on the choice of auxiliary distribution and whether to use restricted or unrestricted estimates in the bootstrap data-generating process.
KW - Clustered data
KW - Cluster-robust variance estimator
KW - Edgeworth expansion
KW - Inference
KW - Wild cluster bootstrap
U2 - 10.1016/j.jeconom.2019.04.035
DO - 10.1016/j.jeconom.2019.04.035
M3 - Journal article
VL - 212
SP - 393
EP - 412
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -