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Focused econometric estimation for noisy and small datasets: A Bayesian Minimum Expected Loss estimator approach

Andres Ramirez-Hassan and Manuel Correa-Giraldo
Authors registered in the RePEc Author Service: Andrés Ramírez Hassan ()

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Abstract: Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug-in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug-in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterized by small sample sizes and noisy models. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when datasets are not very informative.

New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2018-09
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