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Bayesian Dynamic Panel-Ordered Probit Model and Its Application to Subjective Well-Being

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Title: Bayesian Dynamic Panel-Ordered Probit Model and Its Application to Subjective Well-Being
Authors: Hasegawa, Hikaru Browse this author →KAKEN DB
Keywords: Generalized Gibbs sampler
Life satisfaction
Markov chain Monte Carlo (MCMC)
Well-being function
Issue Date: Jun-2009
Publisher: Taylor & Francis
Journal Title: Communications in Statistics : Simulation and Computation
Volume: 38
Issue: 6
Start Page: 1321
End Page: 1347
Publisher DOI: 10.1080/03610910902903133
Abstract: This article proposes a Bayesian method for estimating the dynamic panel ordered probit model. We compare four alternative algorithms for the estimation of ordered probit models. Furthermore, this article presents the empirical results of the application of the dynamic ordered probit model to subjective well-being based on the micro-level survey data extracted from the Japanese Panel Survey of Consumers. The results of the application reveal that income and savings have positive effects on life satisfaction, whereas our posterior results show that marriage and labor force participation have negative effects on the same.
Rights: This is an electronic version of an article published in Communications in Statistics : Simulation and Computation, 38(6) June 2009, 1321-1347. Communications in Statistics : Simulation and Computation is available online at: http://www.informaworld.com/openurl?genre=article&issn=0361-0918&volume=38&issue=6&spage=1321
Type: article (author version)
URI: http://hdl.handle.net/2115/43089
Appears in Collections:経済学院・経済学研究院 (Graduate School of Economics and Business / Faculty of Economics and Business) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 長谷川 光

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