were able to signal currency crises reasonably well in-sample, but the forecasting power of these models out-of-sample was …

across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we …

This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips …

. cost and wage vs. employment adjustments in response to cost-push shocks depend – in theoretically sensible ways – on the … institutional features of firms and of their environment. Focusing on the passthrough of cost shocks to prices, our results suggest …

-month interest rates is explored in an exercise of out-of-sample forecasting. This yield spread appears to contain …

equilibrium models, estimated using Bayesian techniques, can become an additional useful tool in the forecasting kit of central … banks. First, we show that the forecasting performance of such models compares well with a-theoretical vector …

use of measures of underlying in?ation to formulate monetary policy and assist in forecasting observed in?ation. Recent … work has concentrated on modelling large datasets using factor models. In this paper we estimate factors from datasets of … disaggregated price indices for European countries. We then assess the forecasting ability of these factor estimates against other …