An important problem facing managers is how to enhance the credibility, or believability, of their earnings forecasts. In this paper, we experimentally test whether a characteristic of a management earnings forecast—namely, whether it is disaggregated—can affect its credibility. We also test whether disaggregation moderates the relation between managerial incentives and forecast credibility. Di...

Data quality affects decision quality. In any forecasting and estimation model, the quality of the forecast data or the estimated data needs to be evaluated before using for decision-making. In two-level supply chain model, the retailer collects the sales information and use it to forecast the future demands over the lead-time, and the manufacturer collects the information about retailer's orde...

Consensus Economics forecasts for euro-area GDP growth, consumer and producer price inflation and the USD/EUR exchange rate are used by the Czech National Bank to make assumptions about future external economic developments. This paper compares the accuracy of the aforementioned Consensus forecasts to those of the European Commission, International Monetary Fund and Organization for Economic Co...

We study the important problem of how to assure credible forecast information sharing between a supplier and a manufacturer. The supplier is responsible for acquiring the necessary capacity before receiving an order from the manufacturer who possesses private forecast information for her end product. We address how different contracts affect the supplier’s capacity decision and hence the profit...

Catalyzed by the work of Meese and Rogoff (1983), a large literature has documented the inability of empirical models to accurately forecast exchange rates out-of-sample. This paper extends the literature by introducing an empirical strategy that endogenously builds forecast models from a broad set of conventional exchange rate signals. The method is extremely flexible, allowing for potentially...

Fine-grained device-level predictions of both shiftable and nonshiftable energy demand and supply is vital in order to take advantage of Demand Response (DR) for efficient utilization of Renewable Energy Sources. The selection of an effective device-level load forecast model is a challenging task, mainly due to the diversity of the models and the lack of proper tools and datasets that can be us...

Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic wind power forecast scenarios were ge...

In this paper we want to analyse fuzzy weather forecasts, which are computed in our system and used to forecast pollution concentrations. The system works on real data: weather forecasts, meteorological situations and pollution concentrations. We compare defuzzification of the fuzzy weather forecast with weather forecast from Institute of Meteorology and Water Management. This comprehensive ana...

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...

This study analyzes trends in analyst forecast properties from 1987 through 1998 in the United States and seven Pacific Rim countries: Australia, New Zealand, Taiwan, Hong Kong, Japan, South Korea, and Thailand. Analyst forecast properties in the United States have become less dispersed, more accurate, and less optimistic during the sample period. Similar trends exist in Australia and New Zeala...