2026-04-18T04:31:15Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/574792025-12-13T21:20:31Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Mumtaz, Haroon author Petrova, Katerina author 2023-07-06T06:54:53Z 2023-07-06T06:54:53Z 2023 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). time-varying parameters stochastic volatility proxy VAR tax shocks Changing impact of shocks: a time-varying proxy SVAR approach