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Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks

Received: 18 September 2018     Accepted: 16 October 2018     Published: 16 November 2018
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Abstract

Food prices have experienced enormous movements and volatility in the recent past which can be predominantly attributed to climate change. Extreme weather events such as drought, flooding and heat waves have adverse effects on agricultural production in areas where agriculture is weather reliant. Among the extreme weather events experienced in Kenya is a drought in 2008/09 which led to a record increase in food prices. It is against this backdrop that this study sought to investigate the dynamic relationship between maize prices and extreme agro-climatic indicators. The study uses structural vector autoregressive (SVAR) tools; Granger causality, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to examine the dynamic relationship between extreme weather indicators (minimum and maximum temperature and precipitation) and wholesale maize prices. Using different lag length determinant criterion, reduced-form VAR (2) is highlighted as the best model to fit the study data past weather and maize prices information over a data period spanning from January 2000 and December 2016. The study established that there exists granger causality between maize prices and weather variables. Agro-climatic indicators are therefore significant in predicting future maize prices. Principally, this significance can be inferred from the reliance of local agricultural production on phenological patterns. Maize price shocks exhibited inflationary effects on future maize prices, while a shock in weather variables has depreciating effects after three months. With regard to forecast variance, 30-39% of maize price variations resulted from its own shocks. The rest is attributed to precipitation (29-39%); maximum temperature (24-26%); and minimum temperature (7-8%).

Published in International Journal of Data Science and Analysis (Volume 4, Issue 5)
DOI 10.11648/j.ijdsa.20180405.12
Page(s) 79-88
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Structural Vector Autoregressive (SVAR), Granger Causality, Forecast Error Variance Decomposition (FEVD), Impulse Response Function (IRF)

References
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Cite This Article
  • APA Style

    Samuel Waiguru Muriuki, Joseph Kyalo Mung’atu, Antony Gichuhi Waititu. (2018). Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks. International Journal of Data Science and Analysis, 4(5), 79-88. https://doi.org/10.11648/j.ijdsa.20180405.12

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    ACS Style

    Samuel Waiguru Muriuki; Joseph Kyalo Mung’atu; Antony Gichuhi Waititu. Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks. Int. J. Data Sci. Anal. 2018, 4(5), 79-88. doi: 10.11648/j.ijdsa.20180405.12

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    AMA Style

    Samuel Waiguru Muriuki, Joseph Kyalo Mung’atu, Antony Gichuhi Waititu. Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks. Int J Data Sci Anal. 2018;4(5):79-88. doi: 10.11648/j.ijdsa.20180405.12

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  • @article{10.11648/j.ijdsa.20180405.12,
      author = {Samuel Waiguru Muriuki and Joseph Kyalo Mung’atu and Antony Gichuhi Waititu},
      title = {Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks},
      journal = {International Journal of Data Science and Analysis},
      volume = {4},
      number = {5},
      pages = {79-88},
      doi = {10.11648/j.ijdsa.20180405.12},
      url = {https://doi.org/10.11648/j.ijdsa.20180405.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20180405.12},
      abstract = {Food prices have experienced enormous movements and volatility in the recent past which can be predominantly attributed to climate change. Extreme weather events such as drought, flooding and heat waves have adverse effects on agricultural production in areas where agriculture is weather reliant. Among the extreme weather events experienced in Kenya is a drought in 2008/09 which led to a record increase in food prices. It is against this backdrop that this study sought to investigate the dynamic relationship between maize prices and extreme agro-climatic indicators. The study uses structural vector autoregressive (SVAR) tools; Granger causality, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to examine the dynamic relationship between extreme weather indicators (minimum and maximum temperature and precipitation) and wholesale maize prices. Using different lag length determinant criterion, reduced-form VAR (2) is highlighted as the best model to fit the study data past weather and maize prices information over a data period spanning from January 2000 and December 2016. The study established that there exists granger causality between maize prices and weather variables. Agro-climatic indicators are therefore significant in predicting future maize prices. Principally, this significance can be inferred from the reliance of local agricultural production on phenological patterns. Maize price shocks exhibited inflationary effects on future maize prices, while a shock in weather variables has depreciating effects after three months. With regard to forecast variance, 30-39% of maize price variations resulted from its own shocks. The rest is attributed to precipitation (29-39%); maximum temperature (24-26%); and minimum temperature (7-8%).},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks
    AU  - Samuel Waiguru Muriuki
    AU  - Joseph Kyalo Mung’atu
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    Y1  - 2018/11/16
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    DO  - 10.11648/j.ijdsa.20180405.12
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    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20180405.12
    AB  - Food prices have experienced enormous movements and volatility in the recent past which can be predominantly attributed to climate change. Extreme weather events such as drought, flooding and heat waves have adverse effects on agricultural production in areas where agriculture is weather reliant. Among the extreme weather events experienced in Kenya is a drought in 2008/09 which led to a record increase in food prices. It is against this backdrop that this study sought to investigate the dynamic relationship between maize prices and extreme agro-climatic indicators. The study uses structural vector autoregressive (SVAR) tools; Granger causality, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to examine the dynamic relationship between extreme weather indicators (minimum and maximum temperature and precipitation) and wholesale maize prices. Using different lag length determinant criterion, reduced-form VAR (2) is highlighted as the best model to fit the study data past weather and maize prices information over a data period spanning from January 2000 and December 2016. The study established that there exists granger causality between maize prices and weather variables. Agro-climatic indicators are therefore significant in predicting future maize prices. Principally, this significance can be inferred from the reliance of local agricultural production on phenological patterns. Maize price shocks exhibited inflationary effects on future maize prices, while a shock in weather variables has depreciating effects after three months. With regard to forecast variance, 30-39% of maize price variations resulted from its own shocks. The rest is attributed to precipitation (29-39%); maximum temperature (24-26%); and minimum temperature (7-8%).
    VL  - 4
    IS  - 5
    ER  - 

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Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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