Recent empirical work has considered the prediction of inflation by combining the information in a large number of time … the forecasts over a large number of different models, each of which is a linear regression model that relates inflation … pseudo out-of-sample prediction of US inflation, and find that it gives more accurate forecasts than simple equal weighted …

Much research studies US inflation history with a trend-cycle model with unobserved components. A key feature of this … model is that the trend may be viewed as the Fed's evolving inflation target or long-horizon expected inflation. We provide … a new way to measure the slowly evolving trend and the cycle (or inflation gap), based on forecasts from the Survey of …

applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has … and density forecasts for monthly US inflation …

applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has … and density forecasts for monthly US inflation. …

estimates for inflation forecasting both in the short term (one-quarter and one-year ahead) and the medium term (two-year and … measure appears superior to all others in all respects. - Output gap ; real-time data ; euro area ; inflation forecasts ; real …

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 …

We evaluate conditional predictive densities for U.S. output growth and inflation using a number of commonly used … when predicting inflation …

The difficulty in modelling inflation and the significance in discovering the underlying data generating process of … 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 …