Identifying Shocks via Time-Varying Volatility

By Daniel J. Lewis. The Review of Economic Studies, 2019

74 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/74950a258144d5cc95ebe1fdbd412c1206616b2c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Large Order-Invariant Bayesian VARs with Stochastic Volatility (2021, background): “We refer readers to Rigobon (2003), Lanne, Lütkepohl, and Maciejowska (2010) and Lewis (2021) for a more detailed discussion of how identification can be achieved in structural VARs via heteroscedasticity.”
GMM Estimation of Non-Gaussian Structural Vector Autoregression (2019, unknown): “Second, Lewis (2018) considers identiﬁcation based on the autocovariance structure of the second moments of the errors implied by an arbitrary stochastic process for the shock variances without parametric assumptions.”