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  1. Cluster-robust inference
    a guide to empirical practice
    Autor*in: MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, Matthew
    Erschienen: 5-2021
    Verlag:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In... mehr

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    Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In this paper, we use these theoretical results to provide a guide to empirical practice. We do not attempt to present a comprehensive survey of the (very large) literature. Instead, we bridge theory and practice by providing a thorough guide on what to do and why, based on recently available econometric theory and simulation evidence. The paper includes an empirical analysis of the effects of the minimum wage on teenagers using individual data, in which we practice what we preach.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/247198
    Schriftenreihe: Queen's Economics Department working paper ; no. 1456
    Schlagworte: clustered data; grouped data; cluster-robust variance estimator; CRVE; robust inference; wild cluster bootstrap
    Umfang: 1 Online-Ressource (circa 51 Seiten)
  2. Spotlight on researcher decisions
    infrastructure evaluation, instrumental variables, and specification screening
    Autor*in: Ankel-Peters, Jörg; Bensch, Gunther; Vance, Colin
    Erschienen: 2023
    Verlag:  RWI - Leibniz-Institut für Wirtschaftsforschung, Essen, Germany

    This paper revisits the instrumental variable (IV) approach in Lipscomb et al. (2013, 2021, LMB) to study the impacts of electrification. We first make corrections to the construction of the dataset, including the modelled IV. Revised estimates on... mehr

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    This paper revisits the instrumental variable (IV) approach in Lipscomb et al. (2013, 2021, LMB) to study the impacts of electrification. We first make corrections to the construction of the dataset, including the modelled IV. Revised estimates on main outcomes and mechanisms are statistically insignificant, with substantially lower effect sizes. We second develop a framework that accounts for weak IVs and discourages specification screening. Applying it to LMB, we find that most theoretically justified specifications yield insignificant results. The proposed framework is transferable to other IV applications to reduce potential bias stemming from researcher’s or replicator’s discretion. In diesem Papier replizieren wir den Instrumentvariablen-Ansatz (IV) von Lipscomb et al. (2013, 2021) zur Untersuchung der Wirkungen von Elektrifizierung. Wir nehmen zunächst Korrekturen an der Konstruktion des Datensatzes vor, einschließlich der modellierten IV. Die revidierten Schätzungen für die wichtigsten Ergebnisse und Mechanismen der Studie sind statistisch nicht signifikant, mit wesentlich geringeren Effektgrößen. Zweitens entwickeln wir einen Analyserahmen, der schwache IVs berücksichtigt und es erleichtert, Spezifikations-Screening zu vermeiden. Wir stellen bei der Anwendung dieses Analyserahmens fest, dass die meisten theoretisch berechtigten Spezifikationen im Falle von Lipscomb et al. nicht signifikante Ergebnisse liefern. Der vorgeschlagene Analyse-Rahmen ist auf andere Arten von IV-Anwendungen übertragbar, um potenzielle Verzerrungen aufgrund von Ermessensspielräumen auf Seiten der Forschenden und auch Replikatoren zu reduzieren.

     
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    Sprache: Englisch
    Medientyp: Ebook
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    ISBN: 9783969731574
    Weitere Identifier:
    hdl: 10419/269225
    Schriftenreihe: Ruhr economic papers ; #991
    Schlagworte: Replication; instrumental variables; electrification; infrastructure; specification curveanalysis; robust inference
    Umfang: 1 Online-Ressource (circa 38 Seiten), Illustrationen
  3. Cluster-robust inference
    a guide to empirical practice
    Autor*in: MacKinnon, James G.; Nielsen, Morten Ørregaard
    Erschienen: [2022]
    Verlag:  Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark

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    Schriftenreihe: CREATES research paper ; 2022, 08
    Schlagworte: cluster jackknife; clustered data; cluster-robust variance estimator; CRVE,grouped data; robust inference; wild cluster bootstrap
    Umfang: 1 Online-Ressource (circa 59 Seiten), Illustrationen
  4. Leverage, influence, and the jackknife in clustered regression models
    reliable inference using summclust
    Autor*in: MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, Matthew
    Erschienen: 3-2022
    Verlag:  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... mehr

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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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    Weitere Identifier:
    hdl: 10419/260488
    Schriftenreihe: Queen's Economics Department working paper ; no. 1483
    Schlagworte: clustered data; cluster-robust variance estimator; grouped data; highleverageclusters; influential clusters; jackknife; partial leverage; robust inference
    Umfang: 1 Online-Ressource (circa 39 Seiten)
  5. Fairness in incomplete information bargaining: theory and widespread evidence from the field
    Autor*in: Keniston, Daniel; Larsen, Bradley J.; Li, Shengwu; Prescott, James J.; Silveira, Bernardo S.; Yu, Chuan
    Erschienen: 2021
    Verlag:  Stanford Institute for Economic Policy Research (SIEPR), Stanford, CA

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    Schriftenreihe: Working paper / Stanford Institute for Economic Policy Research (SIEPR) ; no. 21, 042 (July, 2021)
    Schlagworte: Bargaining; negotiation; fairness; split-the-difference; incomplete information; robust inference; alternating offers
    Umfang: 1 Online-Ressource (circa 62 Seiten), Illustrationen
  6. Fast cluster bootstrap methods for linear regression models
    Autor*in: MacKinnon, James G.
    Erschienen: 11-2021
    Verlag:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For OLS regression, a new algorithm is provided for the pairs cluster bootstrap, and two algorithms for the wild cluster bootstrap are... mehr

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    Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For OLS regression, a new algorithm is provided for the pairs cluster bootstrap, and two algorithms for the wild cluster bootstrap are compared. One of these is a new way to express an existing algorithm, and the other is new. For IV regression, an algorithm is provided for the wild restricted efficient cluster (WREC) bootstrap, which up to now has been computationally burdensome for large samples. These algorithms are remarkably fast because all computations are based on matrices and vectors that contain sums over the observations within each cluster, which have to be computed just once before the bootstrap loop begins. Monte Carlo experiments are used to study the finite-sample properties of bootstrap Wald tests for OLS regression and of WREC bootstrap tests for IV regression.

     
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    hdl: 10419/247206
    Schriftenreihe: Queen's Economics Department working paper ; no. 1465
    Schlagworte: clustered data; cluster-robust variance estimator; CRVE; robust inference; wild cluster bootstrap; WCR bootstrap; pairs cluster bootstrap; wild restricted efficient cluster bootstrap; WREC bootstrap; bootstrap Wald test
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  7. Cluster-robust jackknife and bootstrap inference for binary response models
    Autor*in: MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, Matthew
    Erschienen: 5-2024
    Verlag:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but... mehr

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    We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves jackknifing at the cluster level. We also propose a linearized version of the cluster-jackknife variance matrix estimator as well as linearized versions of the wild cluster bootstrap. The linearizations are based on empirical scores and are computationally efficient. Throughout we use the logit model as a leading example. We also discuss a new Stata software package called logitjack which implements these procedures. Simulation results strongly favor the new methods, and two empirical examples suggest that it can be important to use them in practice.

     
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    hdl: 10419/301940
    Schriftenreihe: Queen's Economics Department working paper ; no. 1515
    Schlagworte: logit model; logistic regression; clustered data; grouped data; cluster-robust variance estimator; CRVE; cluster jackknife; robust inference; wild cluster boot-strap; linearization
    Umfang: 1 Online-Ressource (circa 47 Seiten), Illustrationen