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动态离散选择模型的半参数贝叶斯估计AI 解读

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…

平滑性与稀疏性下离散—连续分布的自适应贝叶斯估计AI 解读

We consider nonparametric estimation of a mixed discrete‐continuous distribution under anisotropic smoothness conditions and a possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates. Next, we consider a nonparametric mixture of normals model that uses continuous latent variables for the discrete part of the observations. We show that the posterior in this model contracts at…

变维模型的最优辅助先验与可逆跳跃提议AI 解读

This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm…

效用与选择概率映射的满射性AI 解读

This note considers a standard multinomial choice model. It is shown that if the distribution of additive utility shocks has a density, then the mapping from deterministic components of utilities to choice probabilities is surjective. In other words, any vector of choice probabilities can be obtained by selecting suitable utilities for alternatives. This result has implications for at least three areas of interest to econometricians: the Hotz and Miller (1993) estimator for…

动态离散选择模型中期望价值函数的连续性与可微性AI 解读

This paper explores the properties of expected value functions in dynamic discrete choice models. The continuity with respect to state variables and parameters, and the differentiability with respect to state variables are established under fairly general conditions. The differentiability with respect to parameters is proved when some state variables do not affect the state transition probabilities and, thus, the expected value functions. It is shown that such variables are…