The purpose of the paper is to introduce the framework for decomposing the forecast of headline inflation, obtained by … core inflation. The model for inflation decomposition is a small structural model, set up in state space framework. Kalman … filter procedure is applied to filter the future paths of CPI components, given projected headline inflation obtained by …

inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine … learning and econometric methodologies in forecasting the U.S. inflation based on autoregressive and structural models of the … as benchmark. In order to evaluate the contribution of the term-spread in inflation forecasting in different time periods …

We investigate whether fluctuations in U.S. inflation rates are better described by infrequently occurring large shocks … somewhat once we allow for outliers and conditional heteroskedasticity. It appears that, for the purpose of forecasting monthly … U.S. inflation rates, recognizing the distinction between frequent small shocks and infrequent large shocks does not …

This paper presents a framework for quantifying uncertainty around point forecasts for GDP, inflation and house prices …

of US inflation using a model with time-varying mean and variance; we report significant improvements in the forecasting … empirical relevance of the accelerated GARCH updating is illustrated by forecasting daily volatility in return series of all … within a misspecified modelling framework. The score-driven acceleration is also empirically illustrated with the forecasting …

We provide a new way to filter US inflation into trend and cycle components, based on extracting long-run forecasts …, then estimating parameters, and then extracting the stochastic trend in inflation. The trend-cycle model with unobserved … components is consistent with numerous studies of US inflation history and is of interest partly because the trend may be viewed …