Prediction in the random effects model with MA (q) remainder disturbances

Badi H. Baltagi; Long Liu

doi:10.1002/for.1271

Prediction in the random effects model with MA (q) remainder disturbances

Badi H. Baltagi, Long Liu

Department of Economics

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This paper considers the problem of forecasting in a panel data model with random individual effects and MA (q) remainder disturbances. It utilizes a recursive transformation for the MA (q) process derived by Baltagi and Li (Econometric Theory 1994; 10: 396-408) which yields a simple generalized least-squares estimator for this model. This recursive transformation is used in conjunction with Goldberger's result (Journal of the American Statistical Association 1962; 57: 369-375) to derive an analytic expression for the best linear unbiased predictor, for the ith cross-sectional unit, s periods ahead.

Original language	English (US)
Pages (from-to)	333-338
Number of pages	6
Journal	Journal of Forecasting
Volume	32
Issue number	4
DOIs	https://doi.org/10.1002/for.1271
State	Published - Jul 2013

Keywords

MA (q)
panel data
prediction
random effects
serial correlation

ASJC Scopus subject areas

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

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10.1002/for.1271

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AB - This paper considers the problem of forecasting in a panel data model with random individual effects and MA (q) remainder disturbances. It utilizes a recursive transformation for the MA (q) process derived by Baltagi and Li (Econometric Theory 1994; 10: 396-408) which yields a simple generalized least-squares estimator for this model. This recursive transformation is used in conjunction with Goldberger's result (Journal of the American Statistical Association 1962; 57: 369-375) to derive an analytic expression for the best linear unbiased predictor, for the ith cross-sectional unit, s periods ahead.

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KW - random effects

KW - serial correlation

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Prediction in the random effects model with MA (q) remainder disturbances

Abstract

Keywords

ASJC Scopus subject areas

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