Functional Signal plus Noise (FSN) models are proposed for analysing the dynamics of a large cross-section of yields or asset prices in which contemporaneous observations are functionally related. The FSN models are used to forecast high dimensional yield curves for US Treasury bonds at the one month ahead horizon. The models achieve large reductions in mean square forecast errors relative to a...

We compare forecasts of recessions using four different specifications of the probit model: a time invariant conditionally independent version; a business cycle specific conditionally independent model; a time invariant probit with autocorrelated errors; and a business cycle specific probit with autocorrelated errors. The more sophisticated versions of the model take into account some of the po...

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of ...

In order to develop a cane yield forecasting system using crop In order to develop a sugarcane yield forecasting system, a crop yield simulation model is evaluated at the scale of a mill supply area (MSA), using historical climate and yield data. The area1 extent of this scale necessitates the development of spatially distributed model input, both for historical model evaluation and for future ...

Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models propos...

The utilization of NOAA-AVHRR NDVI data for crop yield forecasting is of particular importance in semiarid regions where there are strong inter-year yield fluctuations due to meteorological vagaries. The present work deals with the use of monthly GAC NDVI data for the early estimation of cereal crop yield in Mediterranean African countries. A preliminary analysis showed that relatively high cor...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to ...

We examine the ability of short rates and yield spreads to forecast the growth in Australian industrial output. We find that since 1990, the short rate has a significant increase in its predictive power for forecasting output growth in many industries. We document this increase. The yield spread, on the other hand, is useful in predicting the growth of industries with a `longer' production cycl...