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However, widely used shrinkage estimators guarantee improved precision only under strong distributional assumptions. I develop an estimator for the fixed effects that obtains the best possible mean squared error within a class of shrinkage estimators. This class includes conventional shrinkage estimators and the optimality does not require distributional assumptions. The estimator has an intuitive form and is easy to implement. Moreover, the fixed effects are allowed to vary with time and to be serially correlated, in which case the shrinkage optimally incorporates the underlying correlation structure. I also provide a method to forecast fixed effects one period ahead in this setting.<\/jats:p>","DOI":"10.3982\/ecta22386","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:11:56Z","timestamp":1774591916000},"page":"663-677","source":"Crossref","is-referenced-by-count":2,"title":["Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models"],"prefix":"10.3982","volume":"94","author":[{"given":"Soonwoo","family":"Kwon","sequence":"first","affiliation":[{"name":"Department of Economics, Brown University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2359","published-online":{"date-parts":[[2026]]},"reference":[{"key":"ecta70014-bib-0001","doi-asserted-by":"crossref","unstructured":"Abaluck, Jason, Mauricio M. Caceres Bravo, Peter Hull, and Amanda Starc (2020), \u201cMortality Effects and Choice Across Private Health Insurance Plans.\u201d Tech. Rep. w27578. 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