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Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects

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Author Info
Sune Karlsson ()
Jimmy Skoglund ()

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Abstract

The general case where the time specific effect in a two way model follows an arbitrary ARMA process has not been considered previously. We offer a straightforward maximum likelihood estimator for this case. Allowing for general ARMA processes raises the issue of model specification and we propose tests of the null hypothesis of no serial correlation as well as tests for discriminating between different specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators and test-statistics. Copyright Springer-Verlag 2004

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File URL: http://hdl.handle.net/10.1007/s00181-003-0190-4
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Publisher Info
Article provided by Springer in its journal Empirical Economics.

Volume (Year): 29 (2004)
Issue (Month): 1 (January)
Pages: 79-88
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Handle: RePEc:spr:empeco:v:29:y:2004:i:1:p:79-88

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Related research
Keywords: Panel data; autocorrelation; time specific effect; variance components; C12; C13; C23; C51;

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  1. Giorgio Calzolari & Laura Magazzini, 2009. "Poor identification and estimation problems in panel data models with random effects and autocorrelated errors," Working Papers 53, Università di Verona, Dipartimento di Scienze economiche. [Downloadable!]
  2. Paolo, Foschi, 2005. "Estimating regressions and seemingly unrelated regressions with error component disturbances," MPRA Paper 1424, University Library of Munich, Germany, revised 07 Sep 2006. [Downloadable!]
  3. Jimmy Skoglund & Sune Karlsson, 2002. "Asymptotics for random effects models with serial correlation," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A6-1, International Conferences on Panel Data. [Downloadable!]
  4. Badi H. Baltagi, 2007. "Forecasting with Panel Data," Center for Policy Research Working Papers 91, Center for Policy Research, Maxwell School, Syracuse University. [Downloadable!]
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