All types of end users who must make weatherdependent decisions stand to benefit greatly from knowing the expected accuracy of a particular forecast a priori. Forecast accuracy varies both spatially and temporally as a result of initial state and model errors, which change as the atmospheric flow evolves. Probabilistic weather forecasts derived from numerical weather prediction (NWP) ensembles ...

Testing the out-of-sample forecasting superiority of one model over another requires an a priori partitioning of the data into a model specification /estimation (‘training’) period and a model comparison/evaluation (‘out-of-sample’ or ‘validation’) period. How large a validation period is necessary for a given mean square forecasting error (MSFE) improvement to be statistically significant at t...

The importance of the ET in influencing the dynamics of the atmosphere has become more evident in recent years. The occurrence of ET events over the North Pacific has been observed to coincide with periods of reduced forecast model skill (Jones et al. 2003). Harr et al. (2004) examined downstream propagation of increased ensemble standard deviation in mid-tropospheric heights from several opera...

Forecasting in ation is fundamental to UK monetary policy, both for policy-makers and private agents. However, forecast failure is prevalent with naive devices often outperforming the dominant congruent in-sample model in forecasting competitions. This paper assesses evidence for UK annual and quarterly in ation using the theoretical framework developed by Clements and Hendry (1998, 1999) to ex...

Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used...

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 ...

Extreme value theory has been widely used for modeling the tails of return distribution. Generalized Pareto distribution (GPD) is popularly acknowledged as one of the major tools in Value-at-Risk (VaR) estimation. As Basel II stipulates the significance level for VaR estimation from previous 5% quantile level to more extremal quantile levels at 1%, it demands a more accurate estimation approach...

IN THIS PAPER we derive the asymptotic distribution of multistep prediction from the general dynamic simultaneous equation model. The approach taken is a generalization of that of Schmidt [4] who considered a model with uncorrelated errors. It seems worthwhile to extend Schmidt's results, since the estimation of dynamic simultaneous equation models with autoregressive errors is now fairly commo...