We present a procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation. We demonstrate theoretically, and empirically that this procedure can consistently detect the presence of model misspecification. Our examples demonstrates that this approach delivers good finite-sample performance and is computational less onerous than existing approaches, all of which require re-running the inference algorithm. An empirical application to modelling exchange rate log returns using a g-and-k distribution completes the paper.
翻译:我们提出了一个程序来诊断在使用近似贝叶斯计算法进行推论的情况下的模型误差。 我们从理论上和从经验上证明,这一程序可以始终不断地发现模型误差的存在。 我们的例子表明,这一方法提供了良好的有限抽样性能,而且比现有方法的计算难度小,所有方法都需要重新运行推论算法。 使用g-k分布法模拟汇率日志回报的经验应用完成了论文的完成。