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Prediction from the regression model with two-way error components

✍ Scribed by Eugene Kouassi; Joel Sango; J. M. Bosson Brou; Francis N. Teubissi; Kern O. Kymn


Publisher
John Wiley and Sons
Year
2010
Tongue
English
Weight
212 KB
Volume
30
Category
Article
ISSN
0277-6693

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✦ Synopsis


In this paper we extend the Baillie and Baltagi (1999) paper (Prediction from the regression model with one-way error components. In Analysis of Panels and Limited Dependent Variables Models, Hsiao C, Lahiri K, Lee LF, Pesaran H (eds). Cambridge University Press, Cambridge, UK). In particular, we derive six predictors for the two-way error components model, as well as their associated asymptotic mean squared error (AMSE) of multi-step prediction. In addition, we also provide both theoretical and simulation evidence as to the relative effi ciency of our six alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods which indicate that the ordinary optimal predictors perform well for various accuracy criteria.


πŸ“œ SIMILAR VOLUMES


Prediction from the One-Way Error Compon
✍ Eugene Kouassi; Joel Sango; J.M. Bosson Brou; Francis N. Teubissi; Kern O. Kymn πŸ“‚ Article πŸ“… 2011 πŸ› John Wiley and Sons 🌐 English βš– 149 KB

## ABSTRACT In this paper we extend the works of Baillie and Baltagi (1999, in __Analysis of Panels and Limited Dependent Variables Models__, Hsiao C __et al.__ (eds). Cambridge University Press: Cambridge, UK; 255–267) and generalize certain results from the Baltagi and Li (1992, __Journal of Fore

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## Abstract In this paper we extend Taub (1979) approach for prediction in the context of the variance components model. The extension obtained is based on the two‐way random‐effect model with heteroskedasticity. Prediction functions are then obtained in three heteroskedasticity cases (heteroskedas