Abstract

I propose to identify an SVAR, up to shock ordering, using the autocovariance structure of the squared innovations, implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions on the variance process. In contrast, previous approaches exploiting heteroskedasticity rely on the path of innovation covariances, which can only be recovered from the data under specific parametric assumptions on the variance process. The conditions for identification are testable. I compare the identification scheme to existing approaches in simulations and provide guidance for estimation and inference. I use the methodology to estimate fiscal multipliers peaking at 0.86 for tax cuts and 0.75 for government spending. I find that tax shocks explain more variation in output at longer horizons. The empirical implications of my estimates are more consistent with theory and the narrative record than those based on some leading approaches.

中文翻译：

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通过随时间变化的波动来识别冲击

摘要

我建议使用平方创新的自协方差结构来确定一个SVAR，直到震荡排序为止，这是由随机随机过程对震荡方差所隐含的。这些高阶矩无需方差过程的参数假设即可获得。相反，以前利用异方差性的方法依赖于创新协方差的路径，创新协方差的路径只能在方差过程的特定参数假设下从数据中恢复。识别条件是可测试的。我将识别方案与模拟中的现有方法进行比较，并为估计和推断提供指导。我使用该方法估算出税收乘数的最高乘数是减税和政府支出的乘数0.75。我发现税收冲击可以解释更长时期内产出的更多变化。