Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58708
Title: Analysis of Household Consumption Behavior and Indebted Self-Selection Effects: Case Study of Thailand
Authors: Jianxu Liu
Duangthip Sirikanchanarak
Songsak Sriboonchitta
Jiachun Xie
Keywords: Engineering
Mathematics
Issue Date: 1-Jan-2018
Abstract: © 2018 Jianxu Liu et al. Copula is deemed to be a good approach for relaxing bivariate or multivariate distributions in econometric models. This paper combines static and dynamic copula functions with endogenous switching to study self-selection effects and interdependence between error terms. This technique, copula-based models, is applied to analyze household consumption behavior and indebted self-selection effects in Thailand. The independent, Gaussian, Frank, Clayton, Gumbel, and Joe copula functions and the relatively rotated copula functions were employed in the empirical work. The best model was selected by the information criterion, AIC. We separated the households into four groups, indebted households, debt-free households, households with housing/land loans, and households without housing/land loans, which favors the examination of the treatment effects of indebted households or households with housing debts. The main results indicate that dynamic copula-based models offer better performance than others, such as classical endogenous switching models or all static copula-based models. Also, the I-I and the G-G models underestimate the treatment effects relative to the best models. Additionally, importantly, the traditional normal bivariate distribution or the static copula function could characterize the relationship as regards errors between household debt choice and household consumption and can lead to very misleading implications about the treatment effects.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85048599698&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58708
ISSN: 15635147
1024123X
Appears in Collections:CMUL: Journal Articles

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