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Estimation and Prediction in the Random Effects Model with AR(p) Remainder Disturbances

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Abstract

This paper considers the problem of estimation and forecasting in a panel data model with random individual effects and AR(p) remainder disturbances. It utilizes a simple exact transformation for the AR(p) time series process derived by Baltagi and Li (1994) and obtains the generalized least squares estimator for this panel model as a least squares regression. This exact transformation is also used in conjunction with Goldberger’s (1962) result to derive an analytic expression for the best linear unbiased predictor. The performance of this predictor is investigated using Monte Carlo experiments and illustrated using an empirical example. Key Words: Prediction; Panel Data; Random Effects; Serial Correlation; AR(p) JEL Classification: C32

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  • Badi H. Baltagi & Long Liu, 2012. "Estimation and Prediction in the Random Effects Model with AR(p) Remainder Disturbances," Center for Policy Research Working Papers 138, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:138
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    1. Maeshiro, Asatoshi, 1976. "Autoregressive Transformation, Trended Independent Variables and Autocorrelated Disturbance Terms," The Review of Economics and Statistics, MIT Press, vol. 58(4), pages 497-500, November.
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    5. Nerlove, Marc, 1971. "Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections," Econometrica, Econometric Society, vol. 39(2), pages 359-382, March.
    6. Baltagi, Badi H. & Li, Qi, 1991. "A transformation that will circumvent the problem of autocorrelation in an error-component model," Journal of Econometrics, Elsevier, vol. 48(3), pages 385-393, June.
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    8. Maeshiro, Asatoshi, 1979. "On the Retention of the First Observations in Serial Correlation Adjustment of Regression Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(1), pages 259-265, February.
    9. Frees, Edward W. & Miller, Thomas W., 2004. "Sales forecasting using longitudinal data models," International Journal of Forecasting, Elsevier, vol. 20(1), pages 99-114.
    10. Eugene Kouassi & Joel Sango & J. M. Bosson Brou & Francis N. Teubissi & Kern O. Kymn, 2011. "Prediction from the regression model with two‐way error components," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 541-564, September.
    11. Park, Rolla Edward & Mitchell, Bridger M., 1980. "Estimating the autocorrelated error model with trended data," Journal of Econometrics, Elsevier, vol. 13(2), pages 185-201, June.
    12. Taub, Allan J., 1979. "Prediction in the context of the variance-components model," Journal of Econometrics, Elsevier, vol. 10(1), pages 103-107, April.
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    Cited by:

    1. Simões, Paulo Fernando Mahaz & Souza, Reinaldo Castro & Calili, Rodrigo Flora & Pessanha, José Francisco Moreira, 2020. "Analysis and short-term predictions of non-technical loss of electric power based on mixed effects models," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
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    3. Seth Nana Kwame Appiah-Kubi & Karel Malec & Joseph Phiri & Mansoor Maitah & Zdeňka Gebeltová & Luboš Smutka & Vojtech Blazek & Kamil Maitah & Jitka Sirohi, 2021. "Impact of Tax Incentives on Foreign Direct Investment: Evidence from Africa," Sustainability, MDPI, vol. 13(15), pages 1-12, August.
    4. Seth Nana Kwame Appiah-Kubi & Karel Malec & Mansoor Maitah & Sandra Boatemaa Kutin & Ludmila Pánková & Joseph Phiri & Orhan Zaganjori, 2020. "The Impact of Corporate Governance Structures on Foreign Direct Investment: A Case Study of West African Countries," Sustainability, MDPI, vol. 12(9), pages 1-15, May.
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    Keywords

    prediction; panel data; random effects; serial correlation; ar(p) jel classification: c32;
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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