Factors Explaining the Hypothetical Bias: How to Improve Models for Meta-Analyses

Baoubadi Atozou, Lota Tamini, Stephane Bergeronm and Maurice Doyon

Journal of Agricultural and Resource Economics, 2020, vol. 45, issue 2

Abstract: Using a set of 462 observations from 87 public and private goods economic valuation studies, this study reviews and updates meta-analyses on hypothetical bias using a metaregression hierarchical mixed-effect (MRHME) model that corrects the effects of the unobservable characteristics, within-study error correlation, and potential heteroskedasticity specific to each study. The findings indicate that the MRHME model is more efficient than the log-linear models used in previous meta-analyses. Moreover, this modeling approach and the use of interaction variables by type of goods highlight significant differences relative to previous meta-analyses in the explanatory variables’ effects, significance levels, magnitudes, and signs.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://ageconsearch.umn.edu/record/302460/files/J ... Atozou%2C376-395.pdf (application/pdf)

Related works:
Working Paper: Factors Explaining the Hypothetical Bias: How to Improve Models for Meta-analyses (2019)
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:ags:jlaare:302460

DOI: 10.22004/ag.econ.302460

Access Statistics for this article

More articles in Journal of Agricultural and Resource Economics from Western Agricultural Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().