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The Bootstrap and Multiple Imputations

Harnessing Increased Computing Power for Improved Statistical Tests

Dados Bibliográficos

ID4029809
AutoresDavid Brownstone (University of California, Irvine, California.), Robert G Valletta (Federal Reserve Bank of San Francisco), Robert Valletta (Federal Reserve Bank of San Francisco, San Francisco, California.)
Ano2001
Volume15
Fascículo4
Páginas129-141
Data de publicação2001-11-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoThe Journal of Economic Perspectives (JOURNAL)
Identificadores do periódicoISSN: 0895-3309 • E-ISSN: 1944-7965
EditoraAmerican Economic Association (PUBLISHER • US)
DOI10.1257/jep.15.4.129
OpenAlexW2111886299
IdiomaEN
Citações recebidas19
Referências citadas24

The bootstrap and multiple imputations are two techniques that can enhance the accuracy of estimated confidence bands and critical values. Although they are computationally intensive, relying on repeated sampling from empirical data sets and associated estimates, modern computing power enables their application in a wide and growing number of econometric settings. We provide an intuitive overview of how to apply these techniques, referring to existing theoretical literature and various applied examples to illustrate both their possibilities and their pitfalls

Confidence interval · Data mining · Econometric model · Econometrics · Machine learning · Statistics · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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Obras citantes distintas19
Citações por ano0,86
Intervalo de citações2004 - 2022 (19)
Velocidade de citaçãohistorical
Altamente citadoNão
Tipos de citaçãoNeutras: 19
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