Cluster sample inference using sensitivity analysis: the case with few groups

Johan Vikström ()

No 2009:15, Working Paper Series from IFAU - Institute for Evaluation of Labour Market and Education Policy

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 effects.

Keywords: Cluster-correlation; difference-in-difference; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: C12 C21 C23 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2009-06-11
New Economics Papers: this item is included in nep-ecm
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Published as Vikström, Johan, 'Cluster Sample Inference with Very Few Groups' in Economics Letters, 2016, pages 1-10.

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