The Effects of Dollar/Sterling Exchange Rate Volatility on Futures Markets for Coffee and Cocoa

The paper investigates the extent to which the dollar/sterling exchange rate fluctuations affect coffee and cocoa futures prices on the London LIFFE and the New York CSCE by means of multivariate GARCH models - under the assumption that traders in perfectly competitive markets have equal access to all available information on changes in weather and in global demand and supply conditions. In three out of the four investigated cases, exchange rate posed as a main source of risk for the commodity futures price. The significance and form of volatility spill-over effects of a bilateral exchange rate are shown to be specific for commodity and market. A forecasting comparison on the basis of the identified models suggests that possible gains in prediction accuracy may be small.Commodity markets, Multivariate GARCH models, Exchange rates, Volatility, Forecasting

Inflation in the West African Countries. The Impact of Cocoa Prices, Budget Deficits, and Migrant Remittances

We verify whether cocoa prices could be a source of inflation in five countries of the West African region within a framework that includes other variables such as migrant remittances to the region and a fiscal policy variable represented by the government budget deficit. Unlike earlier studies that explicitly use money supply variables, the inclusion of migrant remittances enables us to examine the effect of an international capital flow variable on inflation. The results reveal that the influence of cocoa prices on consumer price inflation is strong and statistically significant. The influence of the budget deficit and the flow of migrant remittance variables on inflation are, however, weak.Inflation, West Africa, Cocoa, Budget deficits, Remittances

Seasonal Cycles in European Agricultural Commodity Prices

This paper explores the seasonal cycles of European agricultural commodity prices. We focus on three food crops (barley, soft and durum wheat) and on beef. We investigate whether seasonality is deterministic or unit-root stochastic and whether seasonal cycle for specific agricultural commodities have converged over time. Finally, we develop time-series models that are capable of forecasting agricultural prices on a quarterly basis. Firstly, we find that seasonal cycles in agricultural commodity prices are mainly deterministic and that evidence on common cycles across countries varies over agricultural commodities. The prediction experiments, however, yield a ranking with respect to accuracy that does not always match the statistical in-sample evidence.Seasonal cycles, Seasonal unit roots, Forecasting, Agricultural commodities

Toward a Theory of Evaluating Predictive Accuracy

We suggest a theoretical basis for the comparative evaluation of forecasts. Instead of the general assumption that the data is generated from a stochastic model, we classify three stages of prediction experiments: pure non-stochastic prediction of given data, stochastic prediction of given data, and double stochastic simulation. The concept is demonstrated using an empirical example of UK investment data.Forecasting, Time series, Investment

On Mean Reversion in Real Interest Rates: An Application of Threshold Cointegtation

Using data from Germany, Japan, UK, and the U.S., we explore possible threshold cointegration in nominal short- and long-run interest rates with corresponding inflation rates. Traditional cointegration implies perfect mean reversion in real rates and hence confirms the Fisher hypothesis. Threshold cointegration accounts for the possibility that this mean reversion is active only conditional on certain threshold values in the observed variables. We investigate whether findings of such effects can be exploited for interest rate prediction.Nonlinear time series, Fisher equation, Yield spread, Forecasting

Coinfection of cutaneous leishmaniasis and hiv infection

Cutaneous leishmaniasis has recently been discovered in some parts of Ghana. The case of an HIV infected patient presenting with cutaneous leishmaniasis at the Korle-Bu Teaching Hospital is discussed. The diagnosis of leishmaniasis was con-firmed by histology. Also highlighted is the fact that this is the first reported case of dual infection of HIV and Leishmaniasis in Ghana. The possibility of rapid spread to other members of the community, both immunecompetent and immunesuppressed in view of the large numbers of organisms present in the lesion is discussed

Forecasting Seasonally Cointegrated Systems: Supply Response in Austrian Agriculture

This paper examines the relevance of incorporating seasonality in agricultural supply models. Former studies have eliminated the problem of seasonality by using seasonally adjusted data. Recent developments in cointegration techniques allow the comprehensive modelling of error-correcting structures in the presence of seasonality. We consider a four-variables model for Austrian agriculture. Series on the producer price for soy beans, bulls and pigs, as well as the stock of breeding sows are included. A vector autoregression incorporating seasonal cointegration is estimated. A tentative interpretation of long-run and seasonal features is considered. The model is also used to generate forecasts for the supply of pigs in Austria.Seasonality, Agricultural Supply Response, Cointegration, Time Series

Evaluation of the Event Driven Phenology Model Coupled with the VegET Evapotranspiration Model Through Comparisons with Reference Datasets in a Spatially Explicit Manner

A new model coupling scheme with remote sensing data assimilation was developed for estimation of daily actual evapotranspiration (ET). The scheme represents a mix of the VegET, a physically based model to estimate ET from a water balance, and an event driven phenology model (EDPM), where the EDPM is an empirically derived crop specific model capable of producing seasonal trajectories of canopy attributes. In this experiment, the scheme was deployed in a spatially explicit manner within the croplands of the Northern Great Plains. The evaluation was carried out using 2007-2009 land surface forcing data from the North American Land Data Assimilation System (NLDAS) and crop maps derived from remotely sensed data of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compared the canopy parameters produced by the phenology model with normalized difference vegetation index (NDVI) data derived from the MODIS nadir bi-directional reflectance distribution function (BRDF) adjusted reflectance (NBAR) product. The expectations of the EDPM performance in prognostic mode were met, producing determination coefficient (r2) of 0.8 +/-.0.15. Model estimates of NDVI yielded root mean square error (RMSE) of 0.1 +/-.0.035 for the entire study area. Retrospective correction of canopy dynamics with MODIS NDVI brought the errors down to just below 10% of observed data range. The ET estimates produced by the coupled scheme were compared with ones from the MODIS land product suite. The expected r2=0.7 +/-.15 and RMSE = 11.2 +/-.4 mm per 8 days were met and even exceeded by the coupling scheme0 functioning in both prognostic and retrospective modes. Minor setbacks of the EDPM and VegET performance (r2 about 0.5 and additional 30 % of RMSR) were found on the peripheries of the study area and attributed to the insufficient EDPM training and to spatially varying accuracy of crop maps. Overall the experiment provided sufficient evidence of soundness and robustness of the EDPM and VegET coupling scheme, assuring its potential for spatially explicit applications

Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging

Motivated by economic-theory concepts – the Fisher hypothesis and the theory of the term structure – we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK and the US. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models. (Author's abstract