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A revisit to efficient forecasting in linear regression models

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  • Shalabh,

Abstract

This paper deals with the improved forecasts for the values of the study variable in linear regression models utilizing the minimum risk approach. It considers the simultaneous forecasting of actual and average values of the study variable and reports the performance properties of the classical unbiased forecasts and two biased forecasts with respect to the criteria of the bias vector, mean squared error matrix and forecast risk, employing the small disturbance asymptotic theory.

Suggested Citation

  • Shalabh,, 2013. "A revisit to efficient forecasting in linear regression models," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 161-170.
    • Handle: RePEc:eee:jmvana:v:114:y:2013:i:c:p:161-170
    DOI: 10.1016/j.jmva.2012.07.017
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    References listed on IDEAS

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    1. Ullah, A. & Srivastava, V. K. & Chandra, R., 1983. "Properties of shrinkage estimators in linear regression when disturbances are not normal," Journal of Econometrics, Elsevier, vol. 21(3), pages 389-402, April.
    2. Guilkey, David K. & Price, J. Michael, 1981. "On comparing restricted least squares estimators," Journal of Econometrics, Elsevier, vol. 15(3), pages 397-404, April.
    3. Chaturvedi, Anoop & Wan, Alan T. K. & Singh, Shri P., 2002. "Improved Multivariate Prediction in a General Linear Model with an Unknown Error Covariance Matrix," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 166-182, October.
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    Cited by:

    1. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.

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