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Inference with Correlated Clusters

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  • David Powell

Abstract

This paper introduces a method which permits valid inference given a finite number of heterogeneous, correlated clusters. Many inference methods assume clusters are asymptotically independent or model dependence across clusters as a function of a distance metric. With panel data, these restrictions are unnecessary. This paper relies on a test statistic using the mean of the cluster-specific scores normalized by the variance and simulating the distribution of this statistic. To account for cross-cluster dependence, the relationship between each cluster is estimated, permitting the independent component of each cluster to be isolated. The method is simple to implement, can be employed for linear and nonlinear estimators, places no restrictions on the strength of the correlations across clusters, and does not require prior knowledge of which clusters are correlated or even the existence of independent clusters. In simulations, the procedure rejects at the appropriate rate even in the presence of highly-correlated clusters.

Suggested Citation

  • David Powell, 2017. "Inference with Correlated Clusters," Working Papers WR-1137-1, RAND Corporation.
    • Handle: RePEc:ran:wpaper:wr-1137-1
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Finite Inference; Correlated Clusters; Fixed Effects; Panel Data; Hypothesis;
    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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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