Bayesian Estimation of Dynamic Discrete Choice Models
Bayesian Estimation of Dynamic Discrete Choice Models
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DOI：
10.3982/ecta5658
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发表时间：
2009-11
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通讯作者：
Susumu Imai;Neelam Jain;Andrew T. Ching
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作者：
Susumu Imai;Neelam Jain;Andrew T. Ching
提出了一种新的无限时域动态离散选择模型的结构估计方法。我们结合联合收割机的动态规划（DP）解决方案的算法与贝叶斯马尔可夫链蒙特卡罗算法到一个单一的算法，解决DP问题，并估计参数的同时。结果，估计动态模型的计算负担变得与静态模型的计算负担相当。我们的算法的另一个特点是，即使在每个解决方案估计迭代的状态变量上的网格点的数量是小的，有效的网格点的数量随着估计迭代的数量而增加。这就是我们帮助缓解“维度诅咒”的方式。我们模拟和估计几个版本的一个简单的模型的进入和退出，以说明我们的方法。我们还证明，在标准条件下，参数收敛的概率，真正的后验分布，无论起始值。
We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution-estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the “curse of dimensionality.” We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.