Reworking wild bootstrap‐based inference for clustered errors.
Cluster‐robust inference is increasingly common in empirical research. With few clusters, inference is often conducted using the wild cluster bootstrap. With conventional bootstrap weights the set of valid P$$ P $$‐values can create ambiguities in inference. I consider several modifications to the b...
| Publicado en: | Canadian Journal of Economics Vol. 56; no. 3; pp. 839 - 859 |
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| Formato: | Artículo |
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Wiley-Blackwell
Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=170008869&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 170008869 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00084085 CJE jtl: Canadian Journal of Economics issn: 00084085 maglogo: Y pubinfo: dt: Aug2023 vid: 56 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 170008869 10.1111/caje.12661 ppf: 839 ppct: 20 formats: tig: atl: Reworking wild bootstrap‐based inference for clustered errors. aug: au: Webb, Matthew D. affil: Department of Economics, Carleton University su: Probability density function Monte Carlo method sug: subj: Probability density function Monte Carlo method ab: Cluster‐robust inference is increasingly common in empirical research. With few clusters, inference is often conducted using the wild cluster bootstrap. With conventional bootstrap weights the set of valid P$$ P $$‐values can create ambiguities in inference. I consider several modifications to the bootstrap procedure to resolve these ambiguities. Monte Carlo simulations provide evidence that both a new 6‐point bootstrap weight distribution and a kernel density estimation approach improve the reliability of inference. A brief empirical example highlights the implications of these findings. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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