In general, central banks are concerned with keeping the inflation rate stable while also sustaining output close to an efficient level. Under "inflation targeting", forecasts of the evolution of the general price level are an essential input for policy decisions and these are usually released...

Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB’s SPF and Consensus Economics compared to their...

Policymakers and researchers see inflation characterized by cyclical fluctuations driven by changes in resource utilization and temporary shocks, around a trend influenced by inflation expectations. We study the in-sample inflation dynamics and forecast inflation out-of-sample by analyzing a New...

We show that the New Keynesian Phillips Curve (NKPC) outperforms standard benchmarks in forecasting U.S. inflation once frequency-domain information is taken into account. We do so by decomposing the time series (of inflation and its predictors) into several frequency bands and forecasting...

The aim of this paper is to investigate the performance of Phillips curve to forecast inflation in a high inflation emerging market country by taking Turkey as a case. For this purpose we compare the forecasting performance of Phillips curve with alternative time series models namely univariate...

This paper investigates the performance of inflation indicators for predicting the probability of inflation falling inside or outside constant and moving target ranges with a probit model for U.S. data. Particular attention will be given to the moving target range that future inflation will be...

The ability of the New Keynesian Phillips curve to explain US inflation dynamics when official central bank forecasts (Greenbook forecasts) are used as a proxy for inflation expectations is examined. The New Keynesian Phillips curve is estimated on quarterly data spanning the period...

Managing inflation is vital for a stable economy, but forecasting remains challenging. ML methods, like neural networks, have shown promise in forecasting inflation and other macroeconomic variables. In this paper, I propose DPCNet, a deep multi-task learning model, to jointly forecast inflation...

This paper revisits inflation forecasting using reduced-form Phillips curve forecasts, that is, inflation forecasts that use activity and expectations variables. We propose a Phillips-curve-type model that results from averaging across different regression specifications selected from a set of...