Discrete and smooth scalar-on-density compositional regression for assessing the impact of climate change on rice yield in Vietnam

Christine Thomas-Agnan, Michel Simioni and Thi-Huong Trinh

No 23-1410, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: In econometrics, the impact of climate change on agricultural yield has often been modeled using linear functional regression, where crop yield, a scalar response, is regressed on the temperature distribution over a given time period, treated as an ordinary functional parameter, along with other covariates. We explore alternative models that respect the distributional nature of the temperature distribution parameter. Replacing functional observations with the corresponding distributional ones is appropriate for phenomena that are insensitive to the temporal order of events.Since classical addition and scalar multiplication are unsuitable for density functions, alternative operations and spaces are required. Moreover, compositional data analysis suggests that such covariates should undergo appropriate log-ratio transformations before inclusion in the model. We compare a discrete approach, where temperature histograms are treated as compositional vectors, with a smooth scalar-on-density regression using a Bayes space representation of temperature densities. We evaluate the strengths of each method in modeling rice yield in Vietnam, using data on daily temperature extremes.Additionally, we propose modeling climate change scenarios with perturbations of the initial density along a change direction curve informed by IPCC scenarios. The resulting rice yield impact is then quantified using a simple inner product between the density covariate parameter and the change direction curve. Our results indicate that while both approaches yield coherent findings, the smooth model outperforms the discrete one with an enhanced ability to accurately gauge the phenomenon's scale.

Keywords: Compositional scalar-on-density regression; scalar-on-composition regression; Bayes space; compositional splines; functional regression; climate change; rice yield; Vietnam. (search for similar items in EconPapers)
JEL-codes: C14 C16 C39 Q19 Q54 (search for similar items in EconPapers)
Date: 2023-02-09, Revised 2025-06
New Economics Papers: this item is included in nep-agr, nep-env and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.tse-fr.eu/sites/default/files/TSE/docu ... 2023/wp_tse_1410.pdf Full Text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:127847

Access Statistics for this paper

More papers in TSE Working Papers from Toulouse School of Economics (TSE) Contact information at EDIRC.
Bibliographic data for series maintained by ().