Semiparametric Bayesian estimation in an ordinal probit model with application to life satisfaction across countries, age and gender
序数 Probit 模型的半参数贝叶斯估计:跨国、年龄与性别的生活满意度AI 解读 ↓
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序数 Probit 模型的半参数贝叶斯估计:跨国、年龄与性别的生活满意度AI 解读 ↓
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We employ a novel semiparametric Bayesian ordinal probit model to re-examine the relationships between age and life satisfaction (“happiness”) across countries and gender. Within our ordinal choice framework we introduce a new scheme for cutpoint simulation and develop a locally adaptive method for smoothing an otherwise erratic collection of age dummy variable coefficients. We find strong evidence that employment status is deeply intertwined with the reported happiness of men, that employment status is relatively more important for men than it is for women, and that the common use of quadratic models to report ages when happiness is minimized can produce inference that is inaccurate and misleadingly precise. We also find evidence that refines the “U”-shaped pattern between age and happiness that has been reported and/or debated in this literature: For men in most of the European countries we consider, happiness is found to rise sharply before traditional retirement age and then into the beginning of retirement, followed by a flattening or decline in the right-tail of the age distribution. Often-used models that are quadratic in age fail to reproduce this pattern and are frequently at odds with the data.
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动态离散选择模型的半参数贝叶斯估计
We propose a tractable semiparametric estimation method for structural dynamic discrete choice models. The distribution of additive utility shocks in the proposed framework is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions in the multinomial choice settings and the flexibility of the location-scale mixtures. We implement the Bayesian…
动态离散选择模型的半参数贝叶斯估计
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