Calibrating agent-based models (ABMs) in economics and finance typically involves a derivative-free search in a very large parameter space. In this work, we benchmark a number of search methods in the calibration of a well-known macroeconomic ABM on real data, and further assess the performance of "mixed strategies" made by combining different methods. We find that methods based on random-forest surrogates are particularly efficient, and that combining search methods generally increases performance since the biases of any single method are mitigated. Moving from these observations, we propose a reinforcement learning (RL) scheme to automatically select and combine search methods on-the-fly during a calibration run. The RL agent keeps exploiting a specific method only as long as this keeps performing well, but explores new strategies when the specific method reaches a performance plateau. The resulting RL search scheme outperforms any other method or method combination tested, and does not rely on any prior information or trial and error procedure.
翻译:经济和金融中基于校准剂的模型(ABMs)通常涉及在非常大的参数空间进行无衍生物的搜索。在这项工作中,我们将一些搜索方法作为基准,根据真实数据对众所周知的宏观经济反弹道导弹进行校准,并进一步评估以不同方法组合的“混合战略”的性能。我们发现,基于随机森林代孕的方法特别有效,而且由于任何单一方法的偏差都得到缓解,综合搜索方法通常会提高性能。从这些观察中可以看出,我们建议了一个强化学习(RL)方案,以便在校准运行期间自动选择和合并在飞行上搜索的方法。RL代理在使用特定方法时,只要该方法保持运行良好,只要该方法达到性能水平,就继续使用该方法,但探索新的战略。由此产生的RL搜索方案比任何其他经过测试的方法或方法组合都更有效率,并且不依赖任何先前的信息或试验和错误程序。