This article identifies structural vector autoregressions (SVARs) through bound restrictions on the forecast error variance decomposition (FEVD). First, shows FEVD bounds correspond to quadratic inequality columns of rotation matrix transforming reduced-form residuals into shocks. Second, establishes theoretical conditions such that lead a reduction in width impulse response identified set rela...

This article is a theoretical basis for the software implementation of the Physical space Statistical Analysis System PSAS that is used for atmospheric data analysis at the NASA Data Assimilation O ce DAO The PSAS implements a statistical algo rithm that combines irregularly spaced observations with a gridded forecast to produce an optimal estimate of the state of the atmosphere Starting frommo...

This paper derives analytical results for determination of the window size that explores the trade-off between bias and forecast error variance to minimize the mean squared forecast error in the presence of breaks. We show analytically how to determine the estimation window optimally for the case with strictly exogenous regressors. Through Monte Carlo simulations the paper compares the performa...

In the framework of TEI@I methodology, this paper proposes a combined forecast method integrating contextual knowledge (CFMIK). With the help of contextual knowledge, this method considers the effects of those factors that cannot be explicitly included in the forecast model, and thus it can efficiently decrease the forecast error resulted from the irregular events. Through a container throughpu...

Forecasting strategies that are robust to structural breaks have earned renewed attention in the literature. They are built on weighted averages downweighting past information and include forecasting with rolling window, exponential smoothing or exponentially weighted moving average and forecast pooling. These simple strategies are particularly attractive because they are easy to implement, pos...

This paper analyses three Granger noncausality hypotheses within a conditionally Gaussian MS-VAR model. Noncausality in mean is based on Granger’s original concept for linear predictors by defining noncausality from the 1-step ahead forecast error variance for the conditional expectation. Noncausality in mean-variance concerns the conditional forecast error variance, while noncausality in distr...

We examine semiparametric nonlinear autoregressive models with exogenous variables (NLARX) via three classes of artificial neural networks: the first one uses smooth sigmoid activation functions; the second one uses radial basis activation functions; and the third one uses ridgelet activation functions. We provide root mean squared error convergence rates for these ANN estimators of the conditi...

A statistical method referred to as cluster analysis is employed to identify features in forecast and observation fields. These features qualify as natural candidates for events or objects in terms of which verification can be performed. The methodology is introduced and illustrated on synthetic and real quantitative precipitation data. First, it is shown that the method correctly identifies cl...

Very short-period quantitative precipitation forecast (QPF) or nowcast schemes provide deterministic output that fails to convey explicit measures of the uncertainty in the forecast. Presented here is a forecast methodology based upon a Bayesian hierarchical model that produces a QPF product for a 1-h period along with an associated estimated forecast error field. The precipitation forecast qua...