Prediction from the regression model with two-way error components

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 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.

Prediction in an Unbalanced Nested Error Components Panel Data Model

“…), both spatial and serial correlation (Song and Jung, ) and the two‐way error components model (Kouassi et al . ), to mention a few. Applications include Schmalensee et al .…”

“…The basic idea is to derive the best linear unbiased predictor (BLUP) suggested by Goldberger (1962) for the linear regression model but applied to the random-effects panel data model. This BLUP has been extended to handle serial correlation in the remainder error (Baltagi and Li, 1992), spatial correlation (Baltagi and Li, 2004;Baltagi et al, 2012), both spatial and serial correlation (Song and Jung, 2002) and the two-way error components model (Kouassi et al, 2011), to mention a few. Applications include Schmalensee et al (1998), Baltagi et al (2000), Hoogstrate et al (2000), Frees and Miller (2004), Rapach and Wohar (2004) and Brucker and Siliverstovs (2006); see Baltagi (2008) for a recent survey.…”