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Heteroskedastic Price Forecasting for Food Security Management in Developing Countries
TL;DR: In this article, extended GARCH models were used to forecast food price in low-income countries. But the accuracy of the forecast was limited by the limited available data and analysis technologies in low income economies.
Abstract: Price forecasting systems are of considerable importance to food security management by governments' and non-governmental organizations. Sparse data availability in low-income economies, however, generally necessitates reliance on reduced form forecasting methods. Relatively recent innovations in heteroscedasticity-consistent time series techniques offer price forecasting tools that are feasible given available data and analysis technologies in low-income economies. Moreover, extended GARCH models exhibit superior out-of-sample forecast accuracy using monthly food price data from Madagascar. These techniques also permit cost reduction in food security operations by more precise estimation of the risk of hitting a critical price level.
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TL;DR: In this paper, empirical evidence regarding the presence of price thresholds and price volatility in an African maize market was reported. But, the authors did not consider the effect of price volatility on the price of maize.
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TL;DR: This paper developed an empirical model, based on a relatively parsimonious set of regularly measured variables from communities in Kenya's arid north, that generates remarkably accurate forecasts of the likelihood of famine with at least 3 months lead time.
Abstract: Mitigating the negative welfare consequences of crises such as droughts, floods, and disease outbreaks, is a major challenge in many areas of the world, especially in highly vulnerable areas insufficiently equipped to prevent food and livelihood security crisis in the face of adverse shocks. Given the finite resources allocated for emergency response, and the expected increase in incidences of humanitarian catastrophe due to changing climate patterns, there is a need for rigorous and efficient methods of early warning and emergency needs assessment. In this paper we develop an empirical model, based on a relatively parsimonious set of regularly measured variables from communities in Kenya’s arid north, that generates remarkably accurate forecasts of the likelihood of famine with at least 3 months lead time. Such a forecasting model is a potentially valuable tool for enhancing early warning capacity.