Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/207729 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
NBB Working Paper No. 349
Verlag: 
National Bank of Belgium, Brussels
Zusammenfassung: 
This paper proposes a new approach to extract quantile-based inflation risk measures using Quantile Autoregressive Distributed Lag Mixed-Frequency Data Sampling (QADL-MIDAS) regression models. We compare our models to a standard Quantile Auto-Regression (QAR) model and show that it delivers better quantile forecasts at several forecasting horizons. We use the QADL-MIDAS model to construct inflation risk measures proxying for uncertainty, third-moment dynamics and the risk of extreme inflation realizations. We find that these risk measures are linked to the future evolution of inflation and changes in the effective federal funds rate.
Schlagwörter: 
regression quantiles
inflation risk
quantile forecasting
JEL: 
C53
C54
E37
Dokumentart: 
Working Paper

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