Bayesian estimation of agent-based models
Bayesian estimation of agent-based models
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DOI：
10.1016/j.jedc.2017.01.014
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发表时间：
2017-04-01
影响因子：
1.9
通讯作者：
Tsionas, Mike
中科院分区：
文献类型：
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作者：
Grazzini, Jakob;Richiardi, Matteo G.;Tsionas, Mike
我们认为贝叶斯推理技术基于代理（AB）的模型，作为替代模拟最小距离（SMD）。涉及三个计算量大的步骤：（i）模拟模型，（ii）估计可能性和（iii）从参数的后验分布中采样。AB模型的计算复杂性意味着，有效的技术必须使用点（ii）和（iii），可能涉及近似。我们首先讨论非参数（核密度）估计的可能性，加上马尔可夫链蒙特卡罗抽样方案。然后，我们转向似然的参数近似，它可以通过观察模拟结果在统计均衡周围的分布，或者通过假设数据中外部偏差分布的特定形式来推导。最后，我们介绍了近似贝叶斯计算技术的似然自由估计。这些方法允许在贝叶斯框架中嵌入SMD方法，并且特别适合需要鲁棒估计的情况。这些技术首先在一个简单的价格发现模型中进行测试，然后用于估计De Grauwe（2012）的行为宏观经济模型，其中有9个未知参数。(C)2017爱思唯尔B.V.保留所有权利。
We consider Bayesian inference techniques for agent-based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters. (C) 2017 Elsevier B.V. All rights reserved.