Bayesian Kinked Regression with Unobserved Thresholds: An Application to the von Liebig Hypothesis
Austin Ramsey and
Clinton L. Neill
American Journal of Agricultural Economics, 2021, vol. 103, issue 5, 1832-1856
We consider the Bayesian estimation of continuous regression models where the cutoff points that separate different slope regimes are unknown but related to an observed input. Using plateau and von Liebig production functions as examples, we compare the performance of Bayesian mixture models that explicitly estimate regime membership probabilities to Bayesian threshold models that instead treat the unobserved threshold input as a model parameter. Using simulated data as well as actual data from two crop response trials, we show that the threshold model generally outperforms the mixture model in terms of estimation efficiency and predictive accuracy. We also illustrate how Bayesian model averaging can be employed when model performance is less clear cut. Our estimation framework is suitable for a wide range of applications in agricultural and resource economics —as well as other fields.
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:wly:ajagec:v:103:y:2021:i:5:p:1832-1856
Access Statistics for this article
More articles in American Journal of Agricultural Economics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().