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Using time series structural characteristics to analyze grain prices in food insecure countries

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  • Frank Davenport
  • Chris Funk

Abstract

Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security. Copyright Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2015

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  • Frank Davenport & Chris Funk, 2015. "Using time series structural characteristics to analyze grain prices in food insecure countries," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 7(5), pages 1055-1070, October.
    • Handle: RePEc:spr:ssefpa:v:7:y:2015:i:5:p:1055-1070
    DOI: 10.1007/s12571-015-0490-5
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    2. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    3. Massimo Peri, 2017. "Climate variability and the volatility of global maize and soybean prices," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(4), pages 673-683, August.
    4. Alejandro Acosta & Rico Ihle & Stephan Cramon-Taubadel, 2019. "Combining market structure and econometric methods for pricetransmission analysis," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(4), pages 941-951, August.

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