🤖 AI Summary
This study addresses the instability of conventional local projection instrumental variables (LP-IV) estimators in finite samples, particularly the large estimation errors in medium- to long-run impulse responses and the difficulty of conducting joint inference. The authors propose a quasi-Bayesian LP-IV estimator that constructs a moment-based quasi-posterior distribution from the generalized method of moments (GMM) objective function and, for the first time, incorporates a roughness-penalty prior to impose smoothness constraints on multi-horizon responses. While preserving the standard first-order asymptotic properties of traditional LP-IV, the proposed method substantially improves finite-sample stability and enables joint inference via simultaneous confidence bands. Monte Carlo simulations demonstrate markedly lower root mean squared errors, especially at longer horizons, and an empirical application to the Danish electricity market further corroborates its practical relevance.
📝 Abstract
This paper introduces a quasi-Bayesian approach for local projection instrumental-variables (LP-IV) estimation. It builds a moment-based quasi-posterior using the generalized method of moments (GMM) objective and applies a roughness-penalty prior to smooth impulse responses over different horizons. The approach maintains the key first-order features of traditional LP-IV methods, while enhancing stability in finite samples and allowing for joint inference through simultaneous bands. Simulations indicate that this regularization decreases root mean squared error compared to standard GMM, especially at medium and longer horizons. An application to Danish electricity markets highlights the method's practical usefulness.