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Forecasting with Unbalanced Panel Data

Badi Baltagi () and Long Liu ()
Additional contact information
Long Liu: College of Business, University of Texas at San Antonio, https://business.utsa.edu/?s=Long+Liu

No 221, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: 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.

Keywords: Forecasting; BLUP; Unbalanced Panel Data; Unequally Spaced Panels; Serial Correlation (search for similar items in EconPapers)
JEL-codes: C33 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2020-01
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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