{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T11:06:44Z","timestamp":1790334404124,"version":"4.1.0"},"reference-count":56,"publisher":"The Econometric Society","issue":"4","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["QE"],"abstract":"<jats:p>\n                    We develop new econometric methods for estimation and inference in high\u2010dimensional panel data models with interactive fixed effects. Our approach can be regarded as a nontrivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross\u2010sectionally averaged covariates. The unknown parameters are then estimated by applying lasso techniques to the projected model. For inference purposes, we derive a desparsified version of our lasso\u2010type estimator. While the original CCE approach is restricted to the low\u2010dimensional case where the number of regressors is small and fixed, our methods can deal with both low\u2010 and high\u2010dimensional situations where the number of regressors is large and may even exceed the overall sample size. We derive theory for our estimation and inference methods both in the large\u2010\n                    <jats:italic>T<\/jats:italic>\n                    \u2010case, where the time\u2010series length\n                    <jats:italic>T<\/jats:italic>\n                    tends to infinity, and in the small\u2010\n                    <jats:italic>T<\/jats:italic>\n                    \u2010case, where\n                    <jats:italic>T<\/jats:italic>\n                    is a fixed natural number. Specifically, we derive the convergence rate of our estimator and show that its desparsified version is asymptotically normal under suitable regularity conditions. The theoretical analysis of the paper is complemented by a simulation study and an empirical application to characteristic based asset pricing.\n                  <\/jats:p>","DOI":"10.3982\/qe2308","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T13:54:47Z","timestamp":1763387687000},"page":"1457-1509","source":"Crossref","is-referenced-by-count":4,"title":["Estimation and inference in high\u2010dimensional panel data models with interactive fixed effects"],"prefix":"10.3982","volume":"16","author":[{"given":"Maximilian","family":"R\u00fccker","sequence":"first","affiliation":[{"name":"Department of Mathematics and Economics, Ulm University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Vogt","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Economics, Ulm 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