Bayesian Estimation of Agent-Based Models via Adaptive Particle Markov Chain Monte Carlo
Bayesian Estimation of Agent-Based Models via Adaptive Particle Markov Chain Monte Carlo
复制标题
基于自适应粒子马尔可夫链蒙特卡罗的基于代理的模型的贝叶斯估计
作者：
T. Lux
在过去的十年里，经济学中基于主体的模型已经达到了成熟的状态，这使得统计推断和这种模型的拟合度的任务被提上了研究界的议事日程。虽然大多数现有的论文都采用基于似然算法或模拟矩估计器的频率估计方法，但这里我们使用马尔科夫链蒙特卡罗方法(MCMC)来探索贝叶斯估计。MCMC估计量设计中的一个主要问题是找到一种参数化法，它能从建议的密度中得到一个合理的新图纸的接受概率。对于基于主体的模型，建议密度及其参数的适当选择变得更加复杂，因为这样的模型通常需要概率的数值近似。这带来了影响接受率的额外因素，因为它还将取决于可能性的近似误差。在本文中，我们利用了MCMC中的一些最新创新：我们结合了Andrieu等人提出的粒子过滤器马尔可夫链蒙特卡罗。(J R Stat Soc B 72(Part 3)：269-342,2010)，建议分布和延迟拒绝的自适应选择，以确定MCMC估计器的适当设计。我们使用两个著名的行为资产定价模型来说明该方法。
Over the last decade, agent-based models in economics have reached a state of maturity that brought the tasks of statistical inference and goodness-of-fit of such models on the agenda of the research community. While most available papers have pursued a frequentist approach adopting either likelihood-based algorithms or simulated moment estimators, here we explore Bayesian estimation using a Markov chain Monte Carlo approach (MCMC). One major problem in the design of MCMC estimators is finding a parametrization that leads to a reasonable acceptance probability for new draws from the proposal density. With agent-based models the appropriate choice of the proposal density and its parameters becomes even more complex since such models often require a numerical approximation of the likelihood. This brings in additional factors affecting the acceptance rate as it will also depend on the approximation error of the likelihood. In this paper, we take advantage of a number of recent innovations in MCMC: We combine Particle Filter Markov Chain Monte Carlo as proposed by Andrieu et al. (J R Stat Soc B 72(Part 3):269–342, 2010) with adaptive choice of the proposal distribution and delayed rejection in order to identify an appropriate design of the MCMC estimator. We illustrate the methodology using two well-known behavioral asset pricing models.
MCMC 可以检测不可识别的模型。
影响因子：
3.4
作者：
Siekmann,Ivo;Sneyd,James;Crampin,EdmundJ
通讯作者：
Crampin,EdmundJ