Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States
Proxy structural vector autoregressions (SVARs) identify structural shocks in vector autoregressions (VARs) with external proxy variables that are correlated with the structural shocks of interest but uncorrelated with other structural shocks. We provide asymptotic theory for proxy SVARs when the VAR innovations and proxy variables are jointly α-mixing. We also prove the asymptotic validity of a residual-based moving block bootstrap (MBB) for inference on statistics that depend jointly on estimators for the VAR coefficients and for covariances of the VAR innovations and proxy variables. These statistics include structural impulse response functions (IRFs). Conversely, wild bootstraps are invalid, even when innovations and proxy variables are either independent and identically distributed or martingale difference sequences, and simulations show that their coverage rates for IRFs can be badly mis-sized. Using the MBB to re-estimate confidence intervals for the IRFs in Mertens and Ravn (2013), we show that inferences cannot be made about the effects of tax changes on output, labor, or investment.
Keywords: fiscal policy, mixing, residual-based moving block bootstrap, structural vector autoregression, tax shocks, wild bootstrap.
JEL Codes: C15, C32, E62, H24, H25, H31, H3
Suggested citation: Jentsch, Carsten, and Kurt G. Lunsford, 2016. “Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States,” Federal Reserve Bank of Cleveland Working Paper, no. 16-19.