{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-and-feasible-estimation-of-generalized","title":"Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-way Fixed Effects","arxiv_id":"1707.01815","date":"2017-07-06","proceeding":null,"authors":["Amrei Stammann"],"abstract":"We present a fast and memory efficient algorithm for the estimation of generalized linear models with an additive separable k-way error component. The brute force approach uses dummy variables to account for the unobserved heterogeneity, but quickly faces computational limits. Thus, we show how a weighted version of the Frisch-Waugh-Lovell theorem combined with the method of alternating projections can be incorporated into a Newton-Raphson algorithm to dramatically reduce the computational costs. The algorithm is especially useful in situations, where generalized linear models with k-way fixed effects based on dummy variables are computationally demanding or even infeasible due to time or memory limitations. In a simulation study and an empirical application we demonstrate the performance of our algorithm.","url_abs":"http://arxiv.org/abs/1707.01815v3","url_pdf":"http://arxiv.org/pdf/1707.01815v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"fast-and-feasible-estimation-of-generalized","repo_url":"https://github.com/amrei-stammann/alpaca","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}