{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T06:02:07Z","timestamp":1794031327109,"version":"4.8.0"},"reference-count":30,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[2011,3,25]],"date-time":"2011-03-25T00:00:00Z","timestamp":1301011200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2011,10]]},"abstract":"<jats:p>In this paper we propose a bootstrap method for panel data linear regression models with individual fixed effects. The method consists of applying the standard moving blocks bootstrap of K\u00fcnsch (1989, <jats:italic>Annals of Statistics<\/jats:italic> 17, 1217\u20131241) and Liu and Singh (1992, in R. LePage &amp; L. Billiard (eds.), <jats:italic>Exploring the Limits of the Bootstrap<\/jats:italic>) to the vector containing all the individual observations at each point in time. We show that this bootstrap is robust to serial and cross-sectional dependence of unknown form under the assumption that <jats:italic>n<\/jats:italic> (the cross-sectional dimension) is an arbitrary nondecreasing function of <jats:italic>T<\/jats:italic> (the time series dimension), where <jats:italic>T<\/jats:italic> \u2192 \u221e, thus allowing for the possibility that both <jats:italic>n<\/jats:italic> and <jats:italic>T<\/jats:italic> diverge to infinity. The time series dependence is assumed to be weak (of the mixing type), but we allow the cross-sectional dependence to be either strong or weak (including the case where it is absent). Under appropriate conditions, we show that the fixed effects estimator (and also its bootstrap analogue) has a convergence rate that depends on the degree of cross-section dependence in the panel. Despite this, the same studentized test statistics can be computed without reference to the degree of cross-section dependence. Our simulation results show that the moving blocks bootstrap percentile-<jats:italic>t<\/jats:italic> intervals have very good coverage properties even when the degree of serial and cross-sectional correlation is large, provided the block size is appropriately chosen.<\/jats:p>","DOI":"10.1017\/s0266466610000630","type":"journal-article","created":{"date-parts":[[2011,3,25]],"date-time":"2011-03-25T11:35:12Z","timestamp":1301052912000},"page":"1048-1082","source":"Crossref","is-referenced-by-count":81,"title":["THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS"],"prefix":"10.1017","volume":"27","author":[{"given":"S\u00edlvia","family":"Gon\u00e7alves","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2011,3,25]]},"reference":[{"key":"S0266466610000630_ref18","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00344"},{"key":"S0266466610000630_ref10","first-page":"235","article-title":"Consistency of kernel estimators of heteroskedastic and autocorrelated covariance matrices","volume":"68","author":"de Jong","year":"2000","journal-title":"Econometrica"},{"key":"S0266466610000630_ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2006.10.009"},{"key":"S0266466610000630_ref28","unstructured":"Moon R. & Weidner M. 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(2004) Bias Reduction for Dynamic Nonlinear Panel Models with Fixed Effects. Manuscript, University of California\u2013Davis."},{"key":"S0266466610000630_ref29","unstructured":"Vogelsang (2008) Panel Regression Inference Robust to Heteroskedasticity, Serial Correlation and General Forms of Spatial Correlation. Mimeo, Michigan State University."},{"key":"S0266466610000630_ref20","doi-asserted-by":"publisher","DOI":"10.1017\/S0266466600004412"},{"key":"S0266466610000630_ref27","first-page":"224","volume-title":"Exploring the Limits of the Bootstrap","author":"Liu","year":"1992"}],"container-title":["Econometric Theory"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0266466610000630","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,27]],"date-time":"2019-04-27T04:28:38Z","timestamp":1556339318000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0266466610000630\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,3,25]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2011,10]]}},"alternative-id":["S0266466610000630"],"URL":"https:\/\/doi.org\/10.1017\/s0266466610000630","relation":{},"ISSN":["0266-4666","1469-4360"],"issn-type":[{"value":"0266-4666","type":"print"},{"value":"1469-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,3,25]]}}}