LSEbootLS: Bootstrap Methods for Regression Models with Locally Stationary
Errors
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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