Arbeitspapier

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

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-indifferencessetting. The method allows for different types of correlation over time and between groups in the cluster effects.

Language
Englisch

Bibliographic citation
Series: Working Paper ; No. 2009:15

Classification
Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Subject
Cluster-correlation
Difference-in-Differences
Sensitivity analysis
Clusteranalyse
Sensitivitätsanalyse
Schätztheorie
Theorie

Event
Geistige Schöpfung
(who)
Vikström, Johan
Event
Veröffentlichung
(who)
Institute for Labour Market Policy Evaluation (IFAU)
(where)
Uppsala
(when)
2009

Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Vikström, Johan
  • Institute for Labour Market Policy Evaluation (IFAU)

Time of origin

  • 2009

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