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 …

We compare the forecasting performances of the classical and the Minnesota-type Bayesian vector autoregressive (VAR … algorithm for variable selection, estimated using Markov Chain Monte Carlo methods. In this regard, we analyze the forecasting … the linear fixed coefficients classical VAR. However, we do not observe marked gains in forecasting power across the …

an ex ante forecasting exercise. Our results highlight the importance of information content in large number of …

density forecasts between the linear and non-linear models. Finally, in a dynamic 25-step ex-ante and interval forecasting …

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

equilibria in response to shocks and the model specification is superior in forecasting performance out of sample to alternative …

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 …

and non-linear forecasting models and univariate and multivariate techniques. The linear models include vector …’s economy. We conclude that non-linear models generally outperform linear models in forecasting future movements in gross gaming …