Efficient estimation of structural VARs with higher-order moments

Abstract

This paper provides a practical standard for efficient implementation of GMM estimation of structural vector autoregressions identified via higher-order moments. We establish conditions under which the structural estimator is adaptive to reduced-form estimation and the conventional weighting is asymptotically optimal. Despite this optimality, its feasible implementation can be unstable because the covariance of the moments involves many higher-order co-moments. We propose exploiting the same identifying assumption for a structured, bias-corrected covariance estimation under both mutual independence and the weaker mean independence, which also allows for common volatility. The resulting estimator is both asymptotically and computationally efficient. Monte Carlo experiments show substantial gains over conventional GMM and performance comparable to likelihood-based estimators under independent shocks. Under common volatility, the mean-independence estimator remains close to the truth while competing estimators are biased. An oil-market application illustrates the practical gains of the approach.