Changing impact of shocks: a time-varying proxy SVAR approach

Abstract

In this paper, we extend the Bayesian Proxy vector autoregression (VAR) model to incorporate time variation in the parameters. A novel Metropolis-within-Gibbs sampling algorithm is provided to approximate the posterior distributions of the model's parameters. Using the proposed algorithm, we estimate the time-varying effects of taxation shocks in the United States and the United Kingdom and find evidence for a decline in the impact of these shocks on output growth.

Katerina Petrova acknowledges support by the Alan Turing Institute under the EPSRC grant EP/N510129/1, the General Directorate for Research in the Government of Catalonia through the Beatriu de Pinós grant 2019/BP/00239, and the Spanish Ministry of Science and Innovation, through the Severo Ochoa Programme for Centres of Excellence in R&D (CEX2019-000915-S).