Two applications of wild bootstrap methods to improve inference in cluster‐IV models

Finlay, Keith; Magnusson, Leandro M.

doi:10.1002/jae.2710

Cited by 7 publications

(9 citation statements)

References 50 publications

(105 reference statements)

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“…The wild restricted efficient cluster (WREC) bootstrap uses one value of v * per cluster instead of one per observation. This method is one of several that were studied in Finlay and Magnusson (2019). It is the only one that will be discussed here.…”

Section: Bootstrap Confidence Intervalsmentioning

confidence: 99%

“…There is an enormous literature on the finite-sample properties of IV estimates, especially when the instruments are weak; see Andrews, Stock and Sun (2019) for a recent survey. However, with the exception of Finlay and Magnusson (2019) and Wang and Zhang (2021), there has been little work on cluster-robust IV bootstrap methods. The experiments in this subsection attempt to remedy this omission, at least to a modest extent.…”

Section: Bootstrap Tests For IV Regressionmentioning

confidence: 99%

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Fast cluster bootstrap methods for linear regression models

MacKinnon

2023

Econometrics and Statistics

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Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For ordinary least squares (OLS) regression, a new algorithm is provided for the pairs cluster bootstrap, along with two algorithms for the wild cluster bootstrap. One of these is a new way to express an existing method. For instrumental variables (IV) regression, an efficient algorithm is provided for the wild restricted efficient cluster (WREC) bootstrap. All computations are based on matrices and vectors that contain sums of squares and cross-products for 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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Section: Bootstrap Confidence Intervalsmentioning

confidence: 99%

Section: Bootstrap Tests For IV Regressionmentioning

confidence: 99%

Fast cluster bootstrap methods for linear regression models

MacKinnon

2023

Econometrics and Statistics

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“…Additionally, one might consider employing an alternative procedure (e.g., see Moreira et al (2009), Davidson and MacKinnon (2010), Finlay and Magnusson (2019) and Young (2021)):…”

Section: Subvector Inferencementioning

confidence: 99%

“…2 See, e.g., Davidson and MacKinnon (2010), Moreira, Porter, and Suarez (2009), Wang and Kaffo (2016), Finlay and Magnusson (2019), and Young (2021), among others.…”

Section: Introductionmentioning

confidence: 99%

Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters

Wang

Zhang

2021

Preprint

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We study the wild bootstrap inference for instrumental variable (quantile) regressions in the framework of a small number of large clusters, in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity. For subvector inference, we show that the wild bootstrap Wald test with or without using the cluster-robust covariance matrix controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. We further develop a wild bootstrap Anderson-Rubin (AR) test for full-vector inference and show that it controls size asymptotically up to a small error even under weak or partial identification for all clusters. We illustrate the good finite-sample performance of the new inference methods using simulations and provide an empirical application to a well-known dataset about U.S. local labor markets.

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“…The WCRE bootstrap can be used with other test statistics. In particular, Finlay and Magnusson () investigate its use with a cluster‐robust version of the AR statistic proposed in Anderson and Rubin (). The boottest package computes WCRE bootstrap tests based on both AR statistics and IV t statistics for the models (18) and (19) and also for similar models with two or more right‐hand‐side endogenous variables .…”

Section: Simultaneous Equationsmentioning

confidence: 99%

How cluster‐robust inference is changing applied econometrics

MacKinnon

2019

Canadian J of Economics

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In many fields of economics, and also in other disciplines, it is hard to justify the assumption that the random error terms in regression models are uncorrelated. It seems more plausible to assume that they are correlated within clusters, such as geographical areas or time periods, but uncorrelated across clusters. It has therefore become very popular to use "clustered" standard errors, which are robust against arbitrary patterns of within-cluster variation and covariation. Conventional methods for inference using clustered standard errors work very well when the model is correct and the data satisfy certain conditions, but they can produce very misleading results in other cases. This paper discusses some of the issues that users of these methods need to be aware of.Résumé. Comment l'inférence robuste à l'échantillonnage est en passe de modifier l'économétrie appliquée. Dans de nombreuses branches de l'économie, mais également dans d'autres disciplines, il apparaît difficile de justifier l'hypothèse selon laquelle les termes d'erreurs aléatoires des modèles de régression sont indépendants. Il semble plus vraisemblable de considérer qu'ils sont corrélés à l'intérieur des échantillonnages, notamment dans le cadre d'aires géographiques ou de périodes de temps, mais qu'ils sont indépendants en dehors. Il est donc devenu très courant d'utiliser les écarts-types d'échantillonnage, lesquels s'avérant robustes contre les modèles arbitraires de variations et de covariations à l'intérieur de ces mêmes échantillons. Les méthodes habituelles utilisant les écarts-types d'échantillonnage pour évaluer l'inférence fonctionnent parfaitement lorsque le modèle est cohérent et que les données satisfont à certaines conditions. Néanmoins, dans d'autres circonstances, ces dernières peuvent aboutir à des résultats trompeurs. Cet article étudie certains des écueils que peuvent rencontrer les utilisateurs de cette méthode, et contre lesquels ils doivent se prémunir.JEL classification: C12, C15, C21, C23, C25, C26

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Two applications of wild bootstrap methods to improve inference in cluster‐IV models

Cited by 7 publications

References 50 publications

Fast cluster bootstrap methods for linear regression models

Fast cluster bootstrap methods for linear regression models

Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters

How cluster‐robust inference is changing applied econometrics

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