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Recently, seminal work by Peng and Yang (2022) has answered this question under nested models with linear orthonormal series expansion. In the current paper, we further respond to this question under linear nested regression models. A more general nested framework, heteroscedastic and autocorrelated random errors, and sparse coefficients are allowed in the current paper, giving a scenario that is more common in practice. A remarkable implication is that MS can be significantly improved by MA under certain conditions. In addition, we further compare MA techniques with different weight sets. Simulation studies illustrate the theoretical findings in a variety of settings.<\/jats:p>","DOI":"10.1017\/s0266466624000355","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T01:47:21Z","timestamp":1736214441000},"page":"1163-1195","source":"Crossref","is-referenced-by-count":2,"title":["FROM MODEL SELECTION TO MODEL AVERAGING: A COMPARISON FOR NESTED LINEAR MODELS"],"prefix":"10.1017","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4563-3605","authenticated-orcid":false,"given":"Wenchao","family":"Xu","sequence":"first","affiliation":[{"name":"Shanghai University of International Business and Economics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of 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