Working Paper Series
IFAU - Institute for Evaluation of Labour Market and Education Policy
Cluster sample inference using sensitivity analysis: the case with few groups
Abstract: This paper re-examines inference for cluster samples.
Sensitivity analysis is proposed as a new method to perform inference when
the number of groups is small. Based on estimations using disaggregated
data, the sensitivity of the standard errors with respect to the variance
of the cluster effects can be examined in order to distinguish a causal
effect from random shocks. The method even handles just-identified models.
One important example of a just-identified model is the two groups and two
time periods difference-in-differences setting. The method allows for
different types of correlation over time and between groups in the cluster
Keywords: Cluster-correlation; difference-in-difference; sensitivity analysis; (follow links to similar papers)
JEL-Codes: C12; C21; C23; (follow links to similar papers)
43 pages, June 11, 2009
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- This paper is published as:
Vikström, Johan, (2016), 'Cluster Sample Inference with Very Few Groups', Economics Letters, Vol. 3, June, No. 1, pages 1-10
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