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Stochastic response restrictions

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  • Haupt, Harry
  • Oberhofer, Walter

Abstract

This paper considers the implementation of prior stochastic information on unknown outcomes of the response variables into estimation and forecasting of systems of linear regression equations in the context of time series, cross sections, pooled and longitudinal data models. The established approach proves particularly useful when only aggregated information on the response variables is available, as is frequently the case in applied statistics. We address the combination of prior stochastic and sample information as an extension of standard Gauss-Markov theory. Prior stochastic information could be given in the form of experts' expectations, or from estimations and/or projections of other models. A classical (i.e. non-Bayesian) regression framework for the incorporation of prior knowledge in generalized least-squares estimation and prediction is developed.

Suggested Citation

  • Haupt, Harry & Oberhofer, Walter, 2005. "Stochastic response restrictions," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 66-75, July.
    • Handle: RePEc:eee:jmvana:v:95:y:2005:i:1:p:66-75
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    References listed on IDEAS

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    1. repec:ebl:ecbull:v:3:y:2002:i:1:p:1-7 is not listed on IDEAS
    2. Dhaene, Geert & Schokkaert, Erik & Van de Voorde, Carine, 2003. "Best affine unbiased response decomposition," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 242-253, August.
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    Cited by:

    1. Singh, Sukhbir & Jain, Kanchan & Sharma, Suresh, 2012. "Using stochastic prior information in consistent estimation of regression coefficients in replicated measurement error model," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 198-212.

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