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A Unified Framework for Estimation in Lognormal Models
Journal article   Peer reviewed

A Unified Framework for Estimation in Lognormal Models

Fengqing Zhang and Jiangtao Gou
Journal of business & economic statistics, v 40(4), pp 1583-1595
02 Oct 2022
DOI: https://doi.org/10.1080/07350015.2021.1952878
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Cobb-Douglas function model Logarithmic transformation Lognormal regression
Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. In this article, we summarize all the existing estimators for lognormal models, which belong to 12 estimator families. As some estimators were only proposed for the independent and identical distribution setting, we further generalize these estimators to accommodate the general loglinear regression setting. Additionally, we propose 19 new estimators based on different optimization criteria. Mostly importantly, we present a unified framework for all the existing and proposed estimators. The application and comparison of the various estimators using a lognormal linear regression model are demonstrated by simulations and data from the Economic Research Service in the United States Department of Agriculture. A general recommendation for choosing an estimator in practice is discussed. An R package to implement 39 estimators is made available on CRAN.

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1 citations in Web of Science
1 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Economics
Social Sciences, Mathematical Methods
Statistics & Probability
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