Evaluation of Dynamic Stochastic General Equilibrium Models Based on Distributional Comparison of Simulated and Historical Data
Evaluation of Dynamic Stochastic General Equilibrium Models Based on Distributional Comparison of Simulated and Historical Data
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DOI：
10.1016/j.jeconom.2005.11.010
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发表时间：
2003-07
期刊：
影响因子：
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通讯作者：
Norman R. Swanson;V. Corradi
中科院分区：
文献类型：
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作者：
Norman R. Swanson;V. Corradi
我们以不同的动态随机一般均衡模型所隐含的联合分布的存在为起点，所有这些模型都可能被错误地指定。我们的目标是将“真实的”联合分布与给定的DSSGE生成的分布进行比较。这是通过比较历史和模拟时间序列的经验联合分布(或可信区间)来实现的。该工具借鉴了Bootstrap理论、Kolmogorov类型检验的最新进展，以及其他关于DSSE评估的工作，旨在比较历史和模拟时间序列的二阶性质。我们首先将一个给定的模型固定为“基准”模型，并与所有“替代”模型进行比较。然后，我们测试是否至少有一个替代模型比基准模型提供了更准确的真实累积分布近似，基准模型的精度是用分布平方误差来衡量的。文中讨论了自举临界值，并给出了一个说明性的例子，在该例子中，允许校准参数略有变化的标准的动态随机一般均衡模型的替代版本的性能同样好。另一方面，当驱动模型的冲击被分配到不可信的方差和/或分布假设时，模型之间存在明显的差异。
We take as a starting point the existence of a joint distribution implied by different dynamic stochastic general equilibrium (DSGE) models, all of which are potentially misspecified. Our objective is to compare “true” joint distributions with ones generated by given DSGEs. This is accomplished via comparison of the empirical joint distributions (or confidence intervals) of historical and simulated time series. The tool draws on recent advances in the theory of the bootstrap, Kolmogorov type testing, and other work on the evaluation of DSGEs, aimed at comparing the second order properties of historical and simulated time series. We begin by fixing a given model as the “benchmark” model, against which all “alternative” models are to be compared. We then test whether at least one of the alternative models provides a more “accurate” approximation to the true cumulative distribution than does the benchmark model, where accuracy is measured in terms of distributional square error. Bootstrap critical values are discussed, and an illustrative example is given, in which it is shown that alternative versions of a standard DSGE model in which calibrated parameters are allowed to vary slightly perform equally well. On the other hand, there are stark differences between models when the shocks driving the models are assigned non-plausible variances and/or distributional assumptions.