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  1. Leverage, influence, and the jackknife in clustered regression models
    reliable inference using summclust
    Author: MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, Matthew
    Published: 3-2022
    Publisher:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    Cluster-robust inference is widely used in modern empirical work in economics and many other disciplines. The key unit of observation is the cluster. We propose measures of "high-leverage" clusters and "influential" clusters for linear regression... more

    Access:
    Verlag (kostenfrei)
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    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 216
    Inter-library loan:
    No inter-library loan

     

    Cluster-robust inference is widely used in modern empirical work in economics and many other disciplines. The key unit of observation is the cluster. We propose measures of "high-leverage" clusters and "influential" clusters for linear regression models. The measures of leverage and partial leverage, and functions of them, can be used as diagnostic tools to identify datasets and regression designs in which cluster-robust inference is likely to be challenging. The measures of influence can provide valuable information about how the results depend on the data in the various clusters. We also show how to calculate two jackknife variance matrix estimators, CV3 and CV3J, as a byproduct of our other computations. All these quantities, including the jackknife variance estimators, are computed in a new Stata package called summclust that summarizes the cluster structure of a dataset.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/260488
    Series: Queen's Economics Department working paper ; no. 1483
    Subjects: clustered data; cluster-robust variance estimator; grouped data; highleverageclusters; influential clusters; jackknife; partial leverage; robust inference
    Scope: 1 Online-Ressource (circa 39 Seiten)
  2. Fast and reliable jackknife and bootstrap methods for cluster-robust inference
    Author: MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, Matthew
    Published: 4-2022
    Publisher:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regres- sion models estimated by least squares. These estimators have previously been com-... more

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 216
    Inter-library loan:
    No inter-library loan

     

    We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regres- sion models estimated by least squares. These estimators have previously been com- putationally infeasible except for small samples. We also propose several new variants of the wild cluster bootstrap, which involve the new CRVEs, jackknife-based bootstrap data-generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/281089
    Series: Queen's Economics Department working paper ; no. 1485
    Subjects: bootstrap; clustered data; grouped data; cluster-robust variance estima-tor; CRVE; cluster sizes; jackknife; wild cluster bootstrap
    Scope: 1 Online-Ressource (circa 35 Seiten), Illustrationen