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...

In this paper, we explore machine learning (ML) methods to improve inflation forecasting in Brazil. An extensive out-of-sample forecasting exercise is designed with multiple horizons, a large database of 501 series, and 50 forecasting methods, including new ML techniques proposed here,...

Anchored inflation expectations are of key importance for monetary policy. If long-terminflation expectations arewell-anchored, they should be unaffected by short-term economic news. This letter introduces newsregressions with multiple endogenous breaks to investigate the de- and re-anchoring of...

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...

We investigate whether fluctuations in U.S. inflation rates are better described by infrequently occurring large shocks or by frequently occurring small shocks. We estimate a model that encompasses the two hypotheses within the framework of non-Gaussian state-space models. Our results indicate...

This paper revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data,...

The back-propagation neural network (BPN) model has been the most popular form of artificial neural network model used for forecasting, particularly in economics and finance. It is a static (feed-forward) model which has a learning process in both hidden and output layers. In this paper, we...