Fast cluster bootstrap methods for linear regression models
| Autor: | James G. MacKinnon |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Statistics and Probability
Statistics::Theory Economics and Econometrics Computation 05 social sciences Monte Carlo method Instrumental variable Regression Statistics::Computation Bootstrapping (electronics) 0502 economics and business Ordinary least squares Linear regression Cluster (physics) Statistics::Methodology Applied mathematics 050207 economics Statistics Probability and Uncertainty 050205 econometrics Mathematics |
| Zdroj: | Econometrics and Statistics. 26:52-71 |
| ISSN: | 2452-3062 |
| DOI: | 10.1016/j.ecosta.2021.11.009 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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