Research fields
  •  
Visa Ämnen

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

Abstract of Working paper 2009:15

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-indifferences
setting. The method allows for different types of correlation over time and between groups in the cluster effects.
Keywords: Cluster-correlation; Difference-in-Differences; Sensitivity analysis. JEL-codes: C12; C21; C23.

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-Differences; Sensitivity analysis.
JEL-codes: C12; C21; C23.


Published by:

Ifau

Changed:

6/3/2010