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Prediction from the regression model with two‐way error components
Author(s) -
Kouassi Eugene,
Sango Joel,
Bosson Brou J. M.,
Teubissi Francis N.,
Kymn Kern O.
Publication year - 2011
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1176
Subject(s) - mean squared prediction error , monte carlo method , mean squared error , statistics , regression , mathematics , ordinary least squares , econometrics , regression analysis , computer science
Abstract 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 efficiency 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. Copyright © 2010 John Wiley & Sons, Ltd.