Maximum-Likelihood Based Inference in the Two-Way Random Effects Model with Serially Correlated Time Effects
AbstractThis 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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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 1178.
Date of creation: 01 Aug 2000
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- Sune Karlsson & Jimmy Skoglund, 2004. "Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects," Empirical Economics, Springer, vol. 29(1), pages 79-88, January.
- Karlsson, Sune & Skoglund, Jimmy, 2000. "Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects," Working Paper Series in Economics and Finance 383, Stockholm School of Economics.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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