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 …

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 …

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

as nonparametric and semiparametric models in forecasting the nominal interest rate setting that describes the South …:01 to 2004:12, we compare the out-of-sample forecasting ability of the models over the period 2005:01 to 2008:12. Our …

This paper evaluates the performance of 11 vector autoregressive models in forecasting 15 macroeconomic variables for …

(Gaussian, Lasso-LARS, Lasso-Landweber) in forecasting the U.S. real house price growth. We also compare results with forecasts …-suited model for forecasting the U.S. real house price. Among the least square models, the individual regression with house price …

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