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.

在过去的十年中，经济学中基于代理的模型已经成熟，这将统计推断和此类模型的拟合优度的任务提上了研究界的议程。虽然大多数可用的论文都采用基于似然的算法或模拟矩估计器的频率论方法，但在这里我们使用马尔可夫链蒙特卡罗方法 (MCMC) 探索贝叶斯估计。MCMC 估计器设计中的一个主要问题是找到一种参数化，从而从提案密度中找到合理的新抽签接受概率。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. 这会带来影响接受率的其他因素，因为它也将取决于似然的近似误差。在本文中，我们利用了 MCMC 的一些最新创新：我们结合了 Andrieu 等人提出的粒子滤波器马尔可夫链蒙特卡罗。(JR Stat Soc B 72(Part 3):269–342, 2010) 自适应选择提案分布和延迟拒绝，以确定 MCMC 估计器的适当设计。我们使用两个众所周知的行为资产定价模型来说明该方法。2010) 自适应选择提议分布和延迟拒绝，以确定 MCMC 估计器的适当设计。我们使用两个众所周知的行为资产定价模型来说明该方法。2010) 自适应选择提议分布和延迟拒绝，以确定 MCMC 估计器的适当设计。我们使用两个众所周知的行为资产定价模型来说明该方法。