Statistical analysis methods are generally derived under the assumption that forecast errors are strictly random and zero in the mean. If the short-term forecast, used as the background eld in the statistical analysis equation, is in fact biased, so will the resulting analysis be biased. The only way to properly account for bias in a statistical analysis is to do so explicitly, by estimating th...

We investigated the impact of assimilating microwave radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) into the Hurricane Weather Research and Forecasting (HWRF) model on the forecast of typhoon tracks over the western Pacific Ocean. The assimilation of MHS observations has positive impacts on the track forecast, but the assimilation of AMSU...

This paper uses univariate and multivariate singular spectrum analysis for predicting the value and the direction of changes in the daily pound / dollar exchange rate. To perform the forecast, we also use the daily dollar exchange rates with respect to Euro and Japanese yen. We use the random walk model as a benchmark model to evaluate performances of the singular spectrum analysis as a predict...

this paper investigates the forecasting performance of different time-varying bvar models for iranian inflation. forecast accuracy of a bvar model with litterman’s prior compared with a time-varying bvar model (a version introduced by doan et al., 1984); and a modified time-varying bvar model, where the autoregressive coefficients are held constant and only the deterministic components are allo...

An outstanding problem with present ENSO forecast systems is that most of them are initialized in an uncoupled manner, that is, no feedbacks are allowed between the ocean and the atmosphere during data assimilation and model initialization. Such an approach may produce realistic initial states, but not necessarily the optimal conditions for skillful forecasts, because model-data mismatch can ca...

This paper investigates the forecasting ability of five different versions of GARCH models. The five GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-X and GARCH-GJR. Forecast errors based on four emerging stock futures portfolio return (based on forecasted hedge ratio) forecasts are employed to evaluate out-ofsample forecasting ability of the five GARCH models. Daily data...

When dealing with market risk under the Basel II Accord, variation pays in the form of lower capital requirements and higher profits. Typically, GARCH type models are chosen to forecast Value-at-Risk (VaR) using a single risk model. In this paper we illustrate two useful variations to the standard mechanism for choosing forecasts, namely: (i) combining different forecast models for each period,...

An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5th generation Penn State Mesoscale Meteorology Model (MM5) as input to the University of Colorado CARMA dust transport model. AFWA undertook a 60 day evaluation of the effectiveness...

Improving the accuracy of forecast models for physical systems such as the atmosphere is a crucial ongoing effort. The primary focus of recent research on these highly nonlinear systems has been errors in state estimation, but as that error has been successfully diminished, the role of model error in forecast uncertainty has duly increased. The present study is an investigation of an empirical ...

The paper constructs various core inflation measures. These include various trimmed means using disaggregated data and a structural VAR estimate of core inflation for Ireland. The ability of these core inflation measures to forecast future headline inflation is compared using a regression model. An ARIMA model fitted to the headline inflation rate is used as the benchmark forecast. The forecast...