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Uncertainty under a multivariate nested-error regression model with logarithmic transformation

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  • Molina, Isabel

Abstract

Assuming a multivariate linear regression model with one random factor, we consider the parameters defined as exponentials of mixed effects, i.e., linear combinations of fixed and random effects. Such parameters are of particular interest in prediction problems where the dependent variable is the logarithm of the variable that is the object of inference. We derive bias-corrected empirical predictors of such parameters. A second order approximation for the mean crossed product error of the predictors of two of these parameters is obtained, and an estimator is derived from it. The mean squared error is obtained as a particular case.

Suggested Citation

  • Molina, Isabel, 2006. "Uncertainty under a multivariate nested-error regression model with logarithmic transformation," DES - Working Papers. Statistics and Econometrics. WS ws066117, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws066117
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    References listed on IDEAS

    as
    1. Baíllo, Amparo & Molina, Isabel, 2005. "Mean squared errors of small area estimators under a unit-level multivariate model," DES - Working Papers. Statistics and Econometrics. WS ws054007, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    3. Eric V. Slud & Tapabrata Maiti, 2006. "Mean‐squared error estimation in transformed Fay–Herriot models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 239-257, April.
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