Model Uncertainty in Panel Vector Autoregressive Models

G. Koop, Dimitris Korobilis
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引用次数: 77

Abstract

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.
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面板向量自回归模型中的模型不确定性
我们开发了面板向量自回归(PVARs)中的贝叶斯模型平均(BMA)或选择(BMS)方法。我们的方法允许我们在限制性pvar的所有可能组合之间进行选择或平均,其中限制涉及到横断面单元之间的相互依赖性和异质性。由此产生的BMA框架可以找到一个简洁的PVAR规范,从而处理过度参数化问题。我们在一个涉及欧元区主权债务危机的应用中使用了这些方法,并表明我们的方法比其他方法表现得更好。我们的发现反驳了对主权债务危机的简单看法,即将欧元区划分为核心国家和外围国家,并担心后者内部的金融传染。
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