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

Samuel Waiguru Muriuki; Joseph Kyalo Mung’atu; Antony Gichuhi Waititu

doi:doi:10.11648/j.ijdsa.20180405.12

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

Samuel Waiguru Muriuki, Joseph Kyalo Mung’atu, Antony Gichuhi Waititu

Published in International Journal of Data Science and Analysis (Volume 4, Issue 5)

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)

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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
AU  - Antony Gichuhi Waititu
Y1  - 2018/11/16
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdsa.20180405.12
DO  - 10.11648/j.ijdsa.20180405.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 79
EP  - 88
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

Samuel Waiguru Muriuki

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Joseph Kyalo Mung’atu

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Antony Gichuhi Waititu

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

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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
AU  - Antony Gichuhi Waititu
Y1  - 2018/11/16
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdsa.20180405.12
DO  - 10.11648/j.ijdsa.20180405.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 79
EP  - 88
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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