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

Author

Listed:
  • Sune Karlsson

    (Stockholm School of Economics)

  • Jimmy Skoglund

    (Stockholm School of Economics)

Abstract

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

Suggested Citation

  • Sune Karlsson & Jimmy Skoglund, 2000. "Maximum-Likelihood Based Inference in the Two-Way Random Effects Model with Serially Correlated Time Effects," Econometric Society World Congress 2000 Contributed Papers 1178, Econometric Society.
    • Handle: RePEc:ecm:wc2000:1178
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    Cited by:

    1. Giorgio Calzolari & Laura Magazzini, 2012. "Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood," Empirical Economics, Springer, vol. 43(1), pages 145-152, August.
    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.
    3. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    4. Pardo Martínez, Clara Inés, 2013. "An analysis of eco-efficiency in energy use and CO2 emissions in the Swedish service industries," Socio-Economic Planning Sciences, Elsevier, vol. 47(2), pages 120-130.
    5. Robert F. Phillips, 2012. "On computing generalized least squares and maximum-likelihood estimates of error-components models with incomplete panels and correlated disturbances," Economics Bulletin, AccessEcon, vol. 32(4), pages 3017-3024.
    6. Pardo Martínez, Clara Inés & Silveira, Semida, 2012. "Analysis of energy use and CO2 emission in service industries: Evidence from Sweden," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5285-5294.
    7. Olivier Armantier & Oliver Richard, 2008. "Domestic airline alliances and consumer welfare," RAND Journal of Economics, RAND Corporation, vol. 39(3), pages 875-904, September.
    8. 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.
    9. Marcel die Dama & Boniface ngah Epo & Galex syrie Soh, 2013. "Developing a two way error component estimation model with disturbances following a special autoregressive (4) for quarterly data," Economics Bulletin, AccessEcon, vol. 33(1), pages 625-634.
    10. Rendao Ye & Ya Lin, 2023. "Relationship Between Interest Rate and Risk of P2P Lending in China Based on the Skew-Normal Panel Data Model," SAGE Open, , vol. 13(4), pages 21582440231, October.

    More about this item

    JEL classification:

    • 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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