Ciclo di seminari dipartimentali a.a. 2018/2019
Seminario nell’ambito del programma STaRs - Supporting Talented Researchers

Forecasting with Unbalanced Panel Data

Interviene: Badi H. Baltagi (Syracuse University, USA)

ABSTRACT

“Forecasting with Unbalanced Panel Data”
Badi H. Baltagi, Long Liuy

This paper derives the best linear unbiased prediction (BLUP) for an unbalanced panel data
model. Starting with a simple error component regression model with unbalanced panel data
and random effects, it generalizes the BLUP derived by Taub (1979) to unbalanced panels. Next
it derives the BLUP for an unequally spaced panel data model with serial correlation of the
AR(1) type in the remainder disturbances considered by Baltagi and Wu (1999). This in turn
extends the BLUP for a panel data model with AR(1) type remainder disturbances derived by
Baltagi and Li (1992) from the balanced to the unequally spaced panel data case. The
derivations are easily implemented and reduce to tractable expressions using an extension of
the Fuller and Battese (1974) transformation from the balanced to the unbalanced panel data
case.
Badi H. Baltagi: Department of Economics and Center for Policy Research, 426 Eggers Hall,
Syracuse University, Syracuse, NY 13244-1020 U.S.A.;
Long Liu: Department of Economics, College of Business, University of Texas at San Antonio,
One UTSA Circle, TX 78249-0633.