Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates based on the results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time.
| Version: | 1.1 |
| Depends: | R (≥ 3.4.0), zoo |
| Published: | 2020-05-10 |
| Author: | Genaro Sucarrat [aut, cre] |
| Maintainer: | Genaro Sucarrat <gsucarrat at gmail.com> |
| BugReports: | https://github.com/gsucarrat/garchx/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://CRAN.R-project.org/package=garchx, http://www.sucarrat.net/ |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | garchx results |
| Reference manual: | garchx.pdf |
| Package source: | garchx_1.1.tar.gz |
| Windows binaries: | r-devel: garchx_1.1.zip, r-release: garchx_1.1.zip, r-oldrel: garchx_1.1.zip |
| macOS binaries: | r-release: garchx_1.1.tgz, r-oldrel: garchx_1.1.tgz |
| Old sources: | garchx archive |
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