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

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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.

Suggested Citation

  • Vikström, Johan, 2009. "Cluster sample inference using sensitivity analysis: the case with few groups," Working Paper Series 2009:15, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    • Handle: RePEc:hhs:ifauwp:2009_015
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    More about this item

    Keywords

    Cluster-correlation; difference-in-difference; sensitivity analysis;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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