forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and … AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for …

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 … forecasting considering the term–spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871 …

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model …). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy …

walk model suggests that the forecasting performance of the monetary model is superior. …

walk model suggests that the forecasting performance of the monetary model is superior. …

interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting … Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed …-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house …

benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1859 …

Traditionally, the literature on forecasting exchange rates with many potential predictors have primarily only … forecasting horizons. …