Bayesian econometrics in agricultural and resource economics

Abstract The Computer Age has enabled an explosion in Bayesian inference. Historically, agricultural and resource economists have contributed to the development and application of Bayesian econometrics. This article describes Bayesian econometrics in agricultural and resource economics and highlights its use. We detail the basics of Bayesian methodology and computation, and provide an accompanying empirical example. We then examine the strengths and weaknesses of the Bayesian approach, particularly for applications in agricultural and resource economics. Lastly, we consider frontier Bayesian methods and how they might be used to obtain improved inference, make more accurate predictions, or solve computational challenges.

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Publication Details

Journal
European Review of Agricultural Economics
Published
2025-11-13
DOI
https://doi.org/10.1093/erae/jbaf060
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
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article

Bayesian econometrics in agricultural and resource economics

A. Ford Ramsey, Jisang Yu, Klaus Moeltner
European Review of Agricultural Economics
Statistical Methods and Bayesian Inference
article

Bayesian econometrics in agricultural and resource economics

A. Ford Ramsey, Jisang Yu, Klaus Moeltner
article en

Abstract

Abstract The Computer Age has enabled an explosion in Bayesian inference. Historically, agricultural and resource economists have contributed to the development and application of Bayesian econometrics. This article describes Bayesian econometrics in agricultural and resource economics and highlights its use. We detail the basics of Bayesian methodology and computation, and provide an accompanying empirical example. We then examine the strengths and weaknesses of the Bayesian approach, particularly for applications in agricultural and resource economics. Lastly, we consider frontier Bayesian methods and how they might be used to obtain improved inference, make more accurate predictions, or solve computational challenges.

European Review of Agricultural EconomicsVol. 53(1)
University of Georgia (US), Korea Food Research Institute (KR), Agricultural University of Georgia (GE), Virginia Tech (US), Agricultural & Applied Economics Association (US), BOKU University (AT)
Openalex Percentile: Top 68%
Statistical Methods and Bayesian Inference
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