Identification of an appropriate low flow forecast model\ud for the Meuse River

This study investigates the selection of an appropriate low flow forecast model for the Meuse\ud River based on the comparison of output uncertainties of different models. For this purpose, three data\ud driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression\ud model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to\ud be represented by the difference between observed and simulated discharge. The results show that the ANN\ud low flow forecast model with one or two input variables(s) performed slightly better than the other statistical\ud models when forecasting low flows for a lead time of seven days. The approach for the selection of an\ud appropriate low flow forecast model adopted in this study can be used for other lead times and river basins\ud as well

Learning to forecast and cyclical behavior of output and inflation

This paper considers a sticky price model with a cash-in-advance constraint where agents forecast inflation rates with the help of econometric models. Agents use least squares learning to estimate two competing models of which one is consistent with rational expectations once learning is complete. When past performance governs the choice of forecast model, agents may prefer to use the inconsistent forecast model, which generates an equilibrium where forecasts are inefficient. While average output and inflation result the same as under rational expectations, higher moments differ substantially: output and inflation show persistence, inflation responds sluggishly to nominal disturbances, and the dynamic correlations of output and inflation match U.S. data surprisingly well

A weather forecast model accuracy analysis and ECMWF enhancement proposal by neural network

This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people's everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.Web of Science1923art. no. 514

Why are survey forecasts superior to model forecasts?

We investigate two characteristics of survey forecasts that are shown to contribute to their superiority over purely model-based forecasts. These are that the consensus forecasts incorporate the effects of perceived changes in the long-run outlook, as well as embodying departures from the path toward the long-run expectation. Both characteristics on average tend to enhance forecast accuracy. At the level of the individual forecasts, there is scant evidence that the second characteristic enhances forecast accuracy, and the average accuracy of the individualforecasts can be improved by applying a mechanical correction. Keywords: consensus forecast, model-based forecasts, long-run expectations.consensus forecast ; model-based forecasts ; long-run expectations JEL Classification: C53 ; E37

Uncovered Interest Parity with Fundamentals: A Brazilian Exchange Rate Forecast Model

One of the most challenging elements of the inflation-targeting framework is the exchange rate forecast. Wadhwani (1999) proposed a UIP, where real variables like the unemployment differential, the current account differential, and the excess return of financial assets affect the expected exchange rate. The objectives of this paper are first to include, as in Wadhwani (1999), some real variables to anchor exchange rate expectations. In our case, the long-run value of the exchange rate is determined by balanced external accounts. Second, we use this approach to simulate the behavior of key macroeconomic variables in an inflation-targeting structural model for Brazil. Finally, we compare the results with those of a random walk specification. The impulse responses under the UIP-with-fundamentals model seemed to be more realistic than those obtained by using other specifications for exchange rate forecasts.