LSEbootLS: Bootstrap Methods for Regression Models with Locally StationaryErrors
Implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.
| Version: | 0.1.0 |
| Depends: | doParallel, R (≥ 2.10) |
| Imports: | foreach,doRNG, stats, parallel,LSTS,tibble,iterators,rlecuyer |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2024-07-01 |
| DOI: | 10.32614/CRAN.package.LSEbootLS |
| Author: | Guillermo Ferreira [aut], Joel Muñoz [aut], Nicolas Loyola [aut, cre] |
| Maintainer: | Nicolas Loyola <nloyola2016 at udec.cl> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Citation: | LSEbootLS citation info |
| CRAN checks: | LSEbootLS results |
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