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.
- Version0.1.0
- R version≥ 2.10
- LicenseGPL (≥ 3)
- Needs compilation?No
- LSEbootLS citation info
- Last release07/01/2024
Team
Nicolas Loyola
Guillermo Ferreira
Show author detailsRolesAuthorJoel Muñoz
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- Depends1 package
- Imports6 packages
- Suggests1 package