https://emiliogcs.r-universe.devRecent package updates in emiliogcsR-universehttps://github.com/emiliogcs.pnghttps://emiliogcs.r-universe.devMon, 09 Nov 2026 00:48:26 GMTemiliogc@bu.edu (Emilio González-Coya)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.https://github.com/r-universe/emiliogcs/actions/runs/42543759941Mon, 09 Nov 2026 00:48:26 GMTfgls0.1.0successhttps://emiliogcs.r-universe.devhttps://github.com/emiliogcs/fglsfgls.Rmdfgls.htmlGetting started with fgls2026-10-22 20:53:322026-10-22 20:53:32heteroskedasticity.Rmdheteroskedasticity.htmlHeteroskedasticity of unknown form (González-Coya and Perron, 2024)2026-10-22 05:47:202026-10-22 05:47:20serial-correlation.Rmdserial-correlation.htmlSerial correlation (Perron and González-Coya, 2026)2026-10-22 05:47:202026-10-22 20:53:32combined.Rmdcombined.htmlSerial correlation and heteroskedasticity together2026-10-22 05:47:202026-10-22 20:53:32validation.Rmdvalidation.htmlValidation: replicating the papers and agreement with pyfgls2026-10-22 05:47:202026-10-23 04:10:56