Quasi-Bayesian Local Projection Instrumental-Variables Method: Application to Renewable Energy and Electricity Prices

TL;DR

This paper presents a quasi-Bayesian LP-IV estimation method that improves stability and inference in renewable energy and electricity price analysis, demonstrated through simulations and Danish market data.

Contribution

It introduces a novel quasi-Bayesian approach with regularization for LP-IV, enhancing finite-sample stability and enabling joint inference.

Findings

Regularization reduces root mean squared error in simulations.

Method maintains key features of traditional LP-IV.

Application to Danish electricity markets demonstrates practical usefulness.

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.