Prediction in the one‐way error component model with serial correlation

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23 Scopus citations

Abstract

This paper derives the best linear unbiased predictor for a one‐way error component model with serial correlation. A transformation derived by Baltagi and Li (1991) is used to show how the forecast can be easily computed from the GLS estimates and residuals. This result is useful for panel data applications which utilize the error component specification and exhibit serial correlation in the remainder disturbance term. Analytical expressions for this predictor are given when the remainder disturbances follow (1) an AR(1) process, (2) an AR(2) process, (3) a special AR(4) process for quarterly data, and (4) an MA(1) process.

Original languageEnglish (US)
Pages (from-to)561-567
Number of pages7
JournalJournal of Forecasting
Volume11
Issue number6
DOIs
StatePublished - 1992
Externally publishedYes

Keywords

  • Autoregressive Moving average
  • Error components
  • Panel data
  • Prediction

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

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