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Prediction from the One‐Way Error Components Model with AR(1) Disturbances
Author(s) -
Kouassi Eugene,
Sango Joel,
Bosson Brou J.M.,
Teubissi Francis N.,
Kymn Kern O.
Publication year - 2012
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.1233
Subject(s) - term (time) , monte carlo method , mean squared error , mean squared prediction error , econometrics , statistics , mathematics , computer science , disturbance (geology) , paleontology , biology , physics , quantum mechanics
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 Forecasting 11 : 561–567) paper accounting for AR(1) errors in the disturbance term. In particular, we derive six predictors for the one‐way error components model, as well as their associated asymptotic mean squared error of multi‐step prediction in the presence of AR(1) errors in the disturbance term. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods and indicates that the ordinary optimal predictor performs well for various accuracy criteria. Copyright © 2011 John Wiley & Sons, Ltd.