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

Andres Ramirez-Hassan, Manuel Correa-Giraldo

arXiv 19 Sep 2018 · Econometrics · 1 citations (OpenAlex)

arXiv:1809.06996 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

31
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44
in-text mentions
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appendix boundary found by appendix_titled_section at “Appendix” · 68% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
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7Judge, G., Carter, R., Griffiths, W., Lutkeploh, H., and Lee, T.-C (1988) Introduction to the Theory and Practice of Econometrics0.51121100%
8Zellner, A (1978) Estimation of functions of population means and regression coefficients including structural coefficients0.51121100%
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Showing the top 10 of 31 scored citations.