Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 294, 1, 2021 DOI: 10.1016/j.ejor.2021.01.031
Accepted author manuscript, 877 KB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Making inference of British household's happiness efficiency
T2 - A Bayesian latent model
AU - Mamatzakis, Emmanuel C.
AU - Tsionas, Mike G.
N1 - This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 294, 1, 2021 DOI: 10.1016/j.ejor.2021.01.031
PY - 2021/10/1
Y1 - 2021/10/1
N2 - In this paper, we propose a novel approach whereby happiness for British households is identified within a latent model frontier analysis using longitudinal data. By doing so we overcome issues related to the measurement of happiness. To estimate happiness frontier and thereby happiness efficiency, we employ a Bayesian inference procedure organized around Sequential Monte Carlo (SMC) particle filtering techniques. In addition, we propose to consider individual-specific characteristics by estimating happiness efficiency models with individual-specific thresholds to happiness. This is the first study that treats happiness as a latent variable and departs from restrictions that happiness efficiency would be time invariant. Our results show that happiness efficiency is related to the welfare loss associated with potentially misusing the resources that British individuals have at their disposal. Key to happiness is to have certain personality traits, such as being agreeable and extravert as they assist efforts to enhance happiness efficiency. On the other hand, being neurotic impairs happiness efficiency.
AB - In this paper, we propose a novel approach whereby happiness for British households is identified within a latent model frontier analysis using longitudinal data. By doing so we overcome issues related to the measurement of happiness. To estimate happiness frontier and thereby happiness efficiency, we employ a Bayesian inference procedure organized around Sequential Monte Carlo (SMC) particle filtering techniques. In addition, we propose to consider individual-specific characteristics by estimating happiness efficiency models with individual-specific thresholds to happiness. This is the first study that treats happiness as a latent variable and departs from restrictions that happiness efficiency would be time invariant. Our results show that happiness efficiency is related to the welfare loss associated with potentially misusing the resources that British individuals have at their disposal. Key to happiness is to have certain personality traits, such as being agreeable and extravert as they assist efforts to enhance happiness efficiency. On the other hand, being neurotic impairs happiness efficiency.
KW - Behavioural or
KW - Happiness
KW - Latent modelling
KW - Bayesian inference
U2 - 10.1016/j.ejor.2021.01.031
DO - 10.1016/j.ejor.2021.01.031
M3 - Journal article
VL - 294
SP - 312
EP - 326
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 1
M1 - 1
ER -