The predictive accuracy of various econometric models, including random walks, vector autoregressive and vector error-correction models, are investigated using daily futures prices of 4 commodities (the S&P500 index, treasury bonds, gold and crude oil). All models are estimated using a rolling window approach, and evaluated by both in-sample and out-of-sample performance measures. The criteria ...

The ensemble Kalman filter (EnKF) is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts. While the accuracy of the forecast error covariance matrix is crucial for achieving accurate forecasts, the estimate given by the EnKF needs to be improved using inflation techn...

Four dimensional variational (4D-Var) Data Assimilation (DA) method is used to find the optimal initial conditions by minimizing cost function in which background information and observations are provided as the input of the cost function. The corrected initial condition based on background error covariance matrix and observations improve the forecast. The targeted observations determined by us...

Four-dimensional variational (4D-Var) data assimilation method is used to find the optimal initial conditions by minimizing a cost function in which background information and observations are provided as the input of the cost function. The optimized initial conditions based on background error covariance matrix and observations improve the forecast. The targeted observations determined by usin...

Three different potential predictors of forecast error – ensemble spread, mean errors of recent forecasts and the local gradient of the predicted field – were compared. The comparison was performed using the forecasts of 500 hPa geopotential and 2-m temperature of the ECMWF ensemble prediction system at lead times of 96, 168 and 240 h, over North America for each day in 2004. Ensemble spread wa...

Four dimensional variational (4D-Var) Data Assimilation (DA) method is used to find the optimal initial conditions by minimizing a cost function in which background information and observations are provided as the input of the cost function. The optimized initial conditions based on background error covariance matrix and observations improve the forecast. The targeted observations determined by...