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Unobserved components with stochastic volatility in U.S. inflation : estimation and signal extraction

Li, Mengheng; Koopman, Siem Jan - 2018

subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of … the observed time series. We develop a simulated maximum likelihood estimation method based on importance sampling and … applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has …

Persistent link: https://www.econbiz.de/10011809984

Chapter 15. Forecasting with Bayesian Vector Autoregression

Karlsson, Sune - 2013

This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by …

Persistent link: https://www.econbiz.de/10014025233

Stochastic volatility models with ARMA innovations : an application to G7 inflation forecasts

Zhang, Bo; Chan, Joshua; Cross, Jamie - In: International journal of forecasting 36 (2020) 4, pp. 1318-1328

Persistent link: https://www.econbiz.de/10012546706

Stochastic volatility models with ARMA innovations : an application to G7 inflation forecasts

Zhang, Bo; Chan, Joshua; Cross, Jamie L. - 2018

Persistent link: https://www.econbiz.de/10012202537

Large mixed-frequency VARs with a parsimonious time-varying parameter structure

Götz, Thomas B.; Hauzenberger, Klemens - 2018

To simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural changes, we introduce a time-varying parameter mixed-frequency VAR. To keep our approach from becoming too complex, we implement time variation parsimoniously: only the intercepts and a common...