Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects
Sune Karlsson () and
Jimmy Skoglund ()
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Jimmy Skoglund: Dept. of Economic Statistics, Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, S-113 83 Stockholm, Sweden
No 383, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics
This paper considers maximum likelihood estimation and inference in the two-way random effects model with serial correlation. We derive a straightforward maximum likelihood estimator when the time-specific component follow an AR(1) or MA(1) process. The estimator is easily generalized to arbitrary stationary and strictly invertible ARMA processes. In addition we consider the model selection problem and derive tests of the null hypothesis of no serial correlation as well as tests for discriminating between the AR(1) and MA(1) specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators, test-statistics and model selection procedures.
Keywords: Panel data; autocorrelation; time specific effect; variance components (search for similar items in EconPapers)
JEL-codes: C12 C13 C23 C51 (search for similar items in EconPapers)
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Published in Empirical Economics, 2004, pages 79-88.
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Journal Article: Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects (2004)
Working Paper: Maximum-Likelihood Based Inference in the Two-Way Random Effects Model with Serially Correlated Time Effects (2000)
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Persistent link: http://EconPapers.repec.org/RePEc:hhs:hastef:0383
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