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fgls: Feasible GLS for Serial Correlation and Heteroskedasticity

Feasible generalized least squares (FGLS) estimators for linear regressions whose errors are serially correlated, heteroskedastic of unknown form, or both. Serial correlation is handled by the autoregressive filter of Perron and González-Coya (2026) <doi:10.1016/j.jeconom.2026.106346>, estimated from a Durbin regression whose order is chosen by an information criterion, with a variance estimator that stays valid when the regressors are only pre-determined rather than strictly exogenous. Heteroskedasticity is handled by the adaptive Lasso estimate of the skedastic function of González-Coya and Perron (2024) <doi:10.1515/jem-2023-0007>, with a bias-corrected sandwich variance. Both corrections can be combined. Benchmark estimators (ordinary least squares with heteroskedasticity and autocorrelation consistent standard errors, Cochrane-Orcutt, infeasible GLS) and the simulation designs of both papers are included.

Version: 0.1.0
Depends: R (≥ 4.1.0)
Imports: glmnet (≥ 4.1), graphics, grDevices, stats, utils
Suggests: broom, e1071, generics, jsonlite, knitr, lmtest, parallel, rmarkdown, sandwich, spelling, testthat (≥ 3.0.0), tibble, xts, zoo
Published: 2026-11-07
DOI: 10.32614/CRAN.package.fgls (may not be active yet)
Author: Emilio González-Coya ORCID iD [aut, cre, cph]
Maintainer: Emilio González-Coya <emiliogc at bu.edu>
BugReports: https://github.com/emiliogcs/fgls/issues
License: MIT + file LICENSE
URL: https://github.com/emiliogcs/fgls, https://emiliogcs.com/fgls/
NeedsCompilation: no
Language: en-US
Citation: fgls citation info
Materials: README, NEWS
CRAN checks: fgls results

Documentation:

Reference manual: fgls.html , fgls.pdf
Vignettes: Serial correlation and heteroskedasticity together (source, R code)
Getting started with fgls (source, R code)
Heteroskedasticity of unknown form (González-Coya and Perron, 2024) (source, R code)
Serial correlation (Perron and González-Coya, 2026) (source, R code)
Validation: replicating the papers and agreement with pyfgls (source, R code)

Downloads:

Package source: fgls_0.1.0.tar.gz
Windows binaries: r-devel: fgls_0.1.0.zip, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): fgls_0.1.0.tgz, r-oldrel (arm64): fgls_0.1.0.tgz, r-release (x86_64): fgls_0.1.0.tgz, r-oldrel (x86_64): fgls_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=fgls to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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