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- W3001715003 abstract "I propose a Bayesian approach to identify vector autoregressive (VAR) models via proxies in a data-rich environment. The setup augments a small-scale VAR model with latent factors . It allows to trace out the responses of disaggregated series in a unified model while controlling for broad economic conditions . The posterior sampler accounts for the estimation uncertainty in these latent factors as well as the measurement precision of the proxy. In a first application to monetary policy , I extract factors from a wide range of real and financial series and find that the effects of monetary policy shocks vary along the yield curve. In a second application to oil market shocks I add disaggregated US series to a standard model of the global oil market. I find that negative news about future oil supply have adverse effects on the US economy." @default.
- W3001715003 created "2020-01-30" @default.
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- W3001715003 date "2021-02-01" @default.
- W3001715003 modified "2023-09-25" @default.
- W3001715003 title "Proxy Vector Autoregressions in a Data-rich Environment" @default.
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- W3001715003 doi "https://doi.org/10.1016/j.jedc.2020.104046" @default.
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