The time-varying evolution of inflation risks
Dimitris Korobilis,
Bettina Landau,
Alberto Musso and
Anthoulla Phella
No 2600, Working Paper Series from European Central Bank
Abstract:
This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to model flexibly the whole distribution of inflation. In order to make our approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state-space form of the TVP quantile regression into an equivalent high-dimensional regression form. An application of this methodology points to a good forecasting performance of quantile regressions with TVPs augmented with specific credit and money-based indicators for the prediction of the conditional distribution of inflation in the euro area, both in the short and longer run, and specifically for tail risks. JEL Classification: C11, C22, C52, C53, C55, E31, E37, E51
Keywords: Bayesian shrinkage; euro area; Horseshoe; inflation tail risks; MCMC; quantile regression; time-varying parameters (search for similar items in EconPapers)
Date: 2021-10
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-eec, nep-ets, nep-for, nep-mac, nep-mon, nep-ore and nep-rmg
Note: 339070
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Citations: View citations in EconPapers (16)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212600
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