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Simulation-based Tests that Can Use Any Number of Simulations
Racine, Jeff; MacKinnon, James G. - 2004
bootstrap tests, is likely to be most useful when simulation is expensive. …
Persistent link: https://www.econbiz.de/10011940649
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Simulation-based Tests that Can Use Any Number of Simulations
Racine, Jeff; MacKinnon, James G. - Economics Department, Queen's University - 2004
bootstrap tests, is likely to be most useful when simulation is expensive. …
Persistent link: https://www.econbiz.de/10005688306
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Computing Numerical Distribution Functions in Econometrics
MacKinnon, James G. - Economics Department, Queen's University - 2001
conduct asymptotic inference, it is therefore necessary to resort to simulation. Techniques that have commonly been used yield … Kiefer, Vogelsang, and Bunzel (2000) for testing linear restrictions in linear regression models. A program to compute P …
Persistent link: https://www.econbiz.de/10005787648
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Wild cluster bootstrap confidence intervals
MacKinnon, James G. - 2014
Confidence intervals based on cluster-robust covariance matrices can be constructed in many ways. In addition to conventional intervals obtained by inverting Wald (t) tests, the paper studies intervals obtained by inverting LM tests, studentized bootstrap intervals based on the wild cluster...
Persistent link: https://www.econbiz.de/10011380809
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Bootstrap tests for overidentification in linear regression models
Davidson, Russell; MacKinnon, James G. - 2014
Despite much recent work on the finite-sample properties of estimators and tests for linear regression models with a … these circumstances. We study asymptotic tests for overidentification in models estimated by instrumental variables and by … correlation between the disturbances of the two equations of the model. Simulation experiments demonstrate that this makes it …
Persistent link: https://www.econbiz.de/10010368288
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Wild Bootstrap Inference for Wildly Different Cluster Sizes
MacKinnon, James G.; Webb, Matthew D. - 2014
The cluster robust variance estimator (CRVE) relies on the number of clusters being large. The precise meaning of 'large' is ambiguous, but a shorthand 'rule of 42' has emerged in the literature. We show that this rule depends crucially on the assumption of equal-sized clusters. Monte Carlo...
Persistent link: https://www.econbiz.de/10010368290
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The subcluster wild bootstrap for few (treated) clusters
MacKinnon, James G.; Webb, Matthew D. - 2016
called the sub- cluster wild bootstrap. In the case of pure treatment models, where all the observations in each cluster are …
Persistent link: https://www.econbiz.de/10011583207
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Inference with large clustered datasets
MacKinnon, James G. - 2016
Inference using large datasets is not nearly as straightforward as conventional econometric theory suggests when the disturbances are clustered, even with very small intra-cluster correlations. The information contained in such a dataset grows much more slowly with the sample size than it would...
Persistent link: https://www.econbiz.de/10011583208
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Validity of Wild Bootstrap Inference with Clustered Errors
Djogbenou, Antoine; MacKinnon, James G.; Nielsen, … - 2017
We study asymptotic inference based on cluster-robust variance estimators for regression models with clustered errors … be generated under sequences of local alternatives. Simulation experiments illustrate the theoretical results and show …
Persistent link: https://www.econbiz.de/10011939434
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Bootstrap and Asymptotic Inference with Multiway Clustering
MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, … - 2017
We study a cluster-robust variance estimator (CRVE) for regression models with clustering in two dimensions that was …
Persistent link: https://www.econbiz.de/10011939437