模型不确定性下脉冲响应的推断
Inference for Impulse Responses under Model Uncertainty
摘要：
在许多宏观经济应用中，脉冲响应的置信区间是通过估计VAR模型的水平来构建的-忽略协整秩的不确定性。我们研究忽略这种不确定性的后果。我们采用了几种方法来处理模型的不确定性，并强调其缺点。我们提出了一种新的方法-加权推理模型似然性（WIMP）-考虑排名的不确定性，在数据驱动的方式。在模拟中，WIMP优于所有其他考虑的方法，提供的间隔是强大的排名不确定性，但不是过于保守。我们还通过重新评估财政政策冲击的影响，研究了等级不确定性对应用宏观经济分析的潜在影响。
英文摘要：
In many macroeconomic applications, confidence intervals for impulse responses are constructed by estimating VAR models in levels - ignoring cointegration rank uncertainty. We investigate the consequences of ignoring this uncertainty. We adapt several methods for handling model uncertainty and highlight their shortcomings. We propose a new method - Weighted-Inference-by-Model-Plausibility (WIMP) - that takes rank uncertainty into account in a data-driven way. In simulations the WIMP outperforms all other methods considered, delivering intervals that are robust to rank uncertainty, yet not overly conservative. We also study potential ramifications of rank uncertainty on applied macroeconomic analysis by re-assessing the effects of fiscal policy shocks.
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