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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.
Getting started
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Package details |
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| Author | Guillermo Ferreira [aut], Joel Muñoz [aut], Nicolas Loyola [aut, cre] |
| Maintainer | Nicolas Loyola <nloyola2016@udec.cl> |
| License | GPL (>= 3) |
| Version | 0.1.0 |
| Package repository | View on CRAN |
| Installation |
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