the bootstrapping technique, allow the ex ante probability of, for example, a negative GDP growth forecast for the current …

analytical tractability. -- ARMA-GARCH models ; neural networks ; nonparametric density estimation ; forecast accuracy ; option …

Abstract A nonparametric method for comparing multiple forecast models is developed and implemented. The hypothesis of … application to inflation forecasting reveals that a very large majority of thousands of forecast models are redundant, leaving …

This paper introduces a new approach to forecast pooling methods based on a nonparametric prior for the weight vector …

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new … its error component is partially unspecified. The statistical properties of the model are discussed and a novel estimation …

point forecast model is estimated, thereby taking advantage of its predictive power. Then, nonparametric estimation of the …Consider forecasting the economic variable Y_{t h} with predictors X_{t}, where h is the forecast horizon. This paper … introduces a semiparametric method that generates forecast intervals of Y_{t h}|X_{t} from point forecast models. First, the …

We consider estimation and inference about the effects of a policy in the absence of a control group. We obtain … the focus should be on forecast unbiasedness rather than accuracy. Correct specification of the forecasting model is not …

simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved … parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast, an …