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Displaying results 1 to 8 of 8.

  1. Inference in instrumental variables models with heteroskedasticity and many instruments
    Author: Crudu, Federico; Mellace, Giovanni; Sándor, Zsolt
    Published: [2017]
    Publisher:  Università di Siena, [Siena]

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Quaderni del Dipartimento di economia politica e statistica ; n. 761 (novembre 2017)
    Subjects: Instrumental variables; heteroskedasticity; many instruments; jackknife; specification tests; overidentification tests
    Scope: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  2. Empirical likelihood for network data
    Author: Matsushita, Yukitoshi; Otsu, Taisuke
    Published: 2023
    Publisher:  LSE, STICERD, London

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Econometrics papers / LSE ; STICERD ; paper number EM629
    Subjects: network data; empirical likelihood; jackknife
    Scope: 1 Online-Ressource (44 Seiten)
  3. Many (weak) judges in judge-leniency designs
    Author: Jochmans, Koen
    Published: October 2023
    Publisher:  [Toulouse School of Economics], [Toulouse]

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Edition: This version: October 10, 2023
    Series: Working papers / Toulouse School of Economics ; no 1481
    Subjects: bias; examiner design; fixed effects; inference; jackknife; weak instruments
    Scope: 1 Online-Ressource (circa 28 Seiten), Illustrationen
  4. 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

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    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)
  5. 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

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    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.

     
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    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
  6. Jackknife, small bandwidth and high-dimensional asymptotics
    Author: Matsushita, Yukitoshi; Otsu, Taisuke
    Published: [2019]
    Publisher:  LSE, STICERD, London

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Econometrics paper ; paper number EM/2019/605
    Subjects: jackknife; empirical likelihood; nonstandard asymptotics
    Scope: 1 Online-Ressource (28 Seiten)
  7. Jackknife Lagrange multiplier test with many weak instruments
    Author: Matsushita, Yukitoshi; Otsu, Taisuke
    Published: [2020]
    Publisher:  LSE, STICERD, London

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Econometrics paper ; paper number EM/2020/613
    Subjects: many instruments; weak instruments; lagrange multiplier test; jackknife
    Scope: 1 Online-Ressource (15 Seiten)
  8. Measuring and communicating uncertainty of poverty indicators at regional level
    Author: Laureti, Tiziana; Benedetti, Ilaria
    Published: 2020
    Publisher:  Publications Office of the European Union, Luxembourg

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    Source: Union catalogues
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9789276283614
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    Edition: 2020 edition
    Series: Statistical working papers / Eurostat
    Subjects: Poverty Indicators; measuring uncertainty; sub-national estimations; linearization; jackknife
    Scope: 1 Online-Ressource (circa 72 Seiten), Illustrationen