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 the fore...

Security analysts generally provide forecasts of earnings for the current period as well as oneyear ahead earnings at fiscal year end. In this study, we derive an estimation procedure, which infers forecast bias from equivalent price expressions that utilize different horizon earnings forecasts. It is well documented that analyst long-horizon earnings forecasts tend to be more optimistic (ex po...

Purpose: The purpose of this paper is to develop a combined model composed of greyforecast model and Logistic-growth-curve model to improve the accuracy of forecast model of cargo throughput for the port. The authors also use the existing data of a current port to verify the validity of the combined model. Design/methodology/approach: A literature review is undertaken to find the appropriate fo...

Due to the rapid increase in deployment and high penetration of solar power generation worldwide, solar power generation forecasting has become critical to variable generation integration planning, and within utility and independent system operator (ISO) operations. Utilities and ISOs require day ahead and hour ahead as well as intra-hour solar power forecasts for core operations solar power pr...

Motivated by the promising results of global-scale ensemble forecasting, a number of groups have attempted mesoscale, short-range ensemble forecasting (SREF), focusing mainly over the eastern half of the United States. To evaluate the performance of mesoscale SREF over the Pacific Northwest and to test the value of using different initial analyses as a means of ensemble forecast generation, a f...

This paper assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. The predictive performance of these approaches is compared using out of sample forecast errors, where a random walk forecast acts as benchmark. It is found that for five major OECD countries, namely Unit...

In applied forecasting, there is a trade-off between in-sample fit and out-ofsample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, o...

Gold price has significant nonlinearity and time-variance with many indeterminate influencing factors. In order to improve the forecast accuracy of gold price, this paper puts forward a gold price forecast model combing projection pursuit with neural network. At first, projection pursuit algorithm is used to screen the influencing factors, and then the influencing factors are used as the input ...

Now-a-days, different sectors of the economy are being significantly affected by the electricity variable. In this research, we analyzed the monthly electricity consumption in Pakistan for the period of January 1990 through December 2011, using linear and non linear modeling techniques. They include ARIMA, Seasonal ARIMA (SARIMA) and ARCH/GARCH models. Electricity consumption model reveals a si...

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence f...