Maximum-Likelihood Based Inference in the Two-Way Random Effects Model with Serially Correlated Time Effects
Sune Karlsson () and
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Jimmy Skoglund: Stockholm School of Economics
No 1178, Econometric Society World Congress 2000 Contributed Papers from Econometric Society
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. Furthermore we 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 and test-statistics
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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:ecm:wc2000:1178
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