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

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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.

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Notes

  1. Famine Early Warning System Network http://www.fews.net/Pages/default.aspx

  2. Auto-regressive Integrated Moving Average. See appendix B for an overview of ARIMA models

  3. Exogenous shocks can also induce non-linearity in price data.

  4. Meaning that the trend and seasonal components have been removed

  5. Their approach differs by metric but generally involves statistical transformations based on similar values from white noise and known benchmark datasets. For more details see Wang et al. (2006) pg. 346.

  6. Given n dimensions (characteristics in this case) the Euclidean distance between two points (time series) i and j can be calculated as: \( d\left(i,j\right){=}^{/}\overline{{\left({i}_1-{j}_1\right)}^2+{\left({i}_2-{j}_2\right)}^2+\dots +{\left({i}_n-{j}_n\right)}^2} \)

  7. Within a cluster we did not aggregate data that are reported in different currencies or volumes. This means we did not aggregate across countries (only within) or across retail and wholesale markets (only within).

  8. The main disadvantage of the MAPE is that it cannot accommodate time series where there are 0 values in the data. Our current evaluation focuses on price series with no 0 values so this is not a problem. For a scale independent measure that can accommodate 0 values, Hyndman and Koehler (2006) propose the Mean Absolute Scaled Error (MASE).

  9. In the real data example where some series are recorded in different units, we only average series together if they are in the same unit.

  10. This index is calculated from major maize, wheat, and rice ports around the world, and does not include sub-Saharan markets

  11. We calculated the metric using the Box-Pierce multivariate portmanteau test (Box and Pierce 1970).

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Acknowledgments

Kathryn Grace provided many insightful comments on an earlier version of this manuscript. This work was supported by US Geological Survey (USGS) cooperative agreement (#G09AC000001), the USGS Climate and Land Use Change program, NASA SERVIR, and NASA grants NNH12ZDA001N- IDS and NNX14AD30G. The Kenyan price data was provided courtesy of Blake Stabler at FEWS NET and the Kenyan Ministry of Agriculture.

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Authors and Affiliations

  1. Department of Geography, Climate Hazards Group, UC Santa Barbara, Santa Barbara, CA, 93106-4060, USA

    Frank Davenport

  2. United States Geological Survey (U.S.G.S), and Department of Geography, University of CA Santa Barbara, Santa Barbara, CA, USA

    Chris Funk

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  1. Frank Davenport
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Correspondence to Frank Davenport.

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Davenport, F., Funk, C. Using time series structural characteristics to analyze grain prices in food insecure countries. Food Sec. 7, 1055–1070 (2015). https://doi.org/10.1007/s12571-015-0490-5

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