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Maximum-Likelihood Based Inference in the Two-Way Random Effects Model with Serially Correlated Time Effects

  • Sune Karlsson

    (Stockholm School of Economics)

  • Jimmy Skoglund

    (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. 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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Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 1178.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:1178
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