Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks
International Journal of Data Science and Analysis
Volume 4, Issue 5, October 2018, Pages: 79-88
Received: Sep. 18, 2018;
Accepted: Oct. 16, 2018;
Published: Nov. 16, 2018
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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
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%).
Samuel Waiguru Muriuki,
Joseph Kyalo Mung’atu,
Antony Gichuhi Waititu,
Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks, International Journal of Data Science and Analysis.
Vol. 4, No. 5,
2018, pp. 79-88.
Akaike. (1976). Canonical Correlation Analysis of Time Series and the Use of an Information Criterion. Systems Identification: Advances and Case Studies. Academic Press, New York.
Baumeister, Christiane, & Kilian, L. (2013). Do Oil Price Increases Cause Higher Food Prices? No. 2013/10. CFS Working Paper.
Ben, & Abdoussallam. (2002). Impact of Climate Change on Agricultural Production in the Sahel Part 1. Methodological Approach and Case Study for Millet in Niger. Climatic Change.
Carter, Colin, Rausser, G., & Smith. A. (2013). The effect of the US Ethanol Mandate on Corn Prices. Department of Agricultural and Resource Economics, University of California, Davis.
Chen, Q., Colson, Escalante, & Wetzstein. (2012). Considering Macroeconomic Indicators in the Food before Fuel Nexus. Energy Economics.
Chuku, Effiong, & Sam. (2010). Oil Price Distortions and their Shorthand Long-Run Impacts on the Nigerian Economy. MPRA Paper No. 24434.
Dinku, Ceccato, Grover-Kopec, Lemma, & Conner. (2007). Validation of Satellite Rainfall Products over East Africa’s Complex Topography. International Journal of Remote Sensing.
Ekpoh, I. (2010). Adaption to The Impact of Climatic Variations on Agriculture by Rural Farmers in North-western Nigeria. J Sustain Dev.
Fackler. (1988). Vector Autoregressive Techniques for Structural Analysis. Rev. Anal. Econ.
FAO. (2013). Rural Youth Employment in Developing Countries: A Global View. Rural Employment Overview/Synthesis No. 1.
FAOSTAT. (2013). Food and Agriculture Organization of the United Nations.
FAOSTAT, D. (2013). Food and Agriculture Organization of the United Nations. Statistical Database.
Granger. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica.
Hannan, & Quinn. (1979). The Determination of the Order of an Auto-regression. Journal of the Royal Statistical Society, Series B 41.
Hosking. (1980). The Multivariate Portmanteau Statistic. Journal of the American Statistical Association.
Hui. (2004). Application of Vector Autoregressive Models to Hong Kong Air Pollution Data. Hong Kong.
Javid, & Munir. (2011). The price puzzle and transmission mechanism of monetary policy in Pakistan: Structural vector autoregressive approach. MPRA Paper No. 30670.
Jayne, Thomas, & Robert. (2006). The Effects of Government Maize Marketing Policies On Maize Market Prices. In Kenya. In Contributed Paper Prepared for Presentation at the International Association of Agricultural Economists’ Conference. Gold Coast, Australia.
Kilavi, M. (2012). Kenya’s Climate. Kenya.
Kimani. (2015). Climate Change on Agricultural: Challenges on Maize Production in Uasin Gishu and Trans Nzoia Counties. Department of Meteorology. University of Nairobi.
Lutkepohl. (2005a). New Introduction to Multiple Time Series Analysis. Springer, Berlin.
Lutz, K. (2009). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. The American Economic Review No. 99.
Maccini, & Yang. (2009). Under The Weather: Health, Schooling, and Economic Consequences of Early-Life Rainfall. American Economic Review.99 (3).
McPhail, Lu, L., Du, X., & Muhammad. A. (2012). Disentangling Corn Price Volatility: The Role of Global Demand, Speculation, and Energy. Journal of Agricultural and Applied Economics.
Melolinna, & Marko. (2012). Macroeconomic Shocks in an Oil Market VAR. European Central Bank.
Mirza, M. (2003). Climate Change and Extreme Weather Events: Can Developing Countries Adapt? Climate Policy.
Murungaru. (2003). Opening Statement by the Minister of State, Oﬃce of the President Republic of Kenya during the Second Conference on Early Warning Systems. Opening-Statement.
Ong’amo, Raĳ, L., Dupas, Moyal, Calatayud, & Silvain. (2006). Distribution, Pest Status and Agro-Climatic Preferences of Lepidopteran Stem Borers of Maize in Kenya. In Annales de la Socie Entomologique de France.
Oseni, & Masarirambi. (2011). Effect of Climate Change on Maize (Zea Mays) Production and Food Security in Swaziland. Change, 2, 3.
Oyinbo, Adegboye, & Sulaiman. (2010). Retrospective Study of Causal Relationship between Climate Variability and Crop Production in Nigeria. Journal of Occupational Safety and Environmental Health.
Rocha, & Soares. (2015). Water Scarcity and Birth Outcomes in the Brazilian Semiarid. Journal of Development Economics.
Rojas, Vrieling, & Rembold. (2011). Assessing Drought Probability for Agricultural Areas in Africa with Coarse Resolution Remote Sensing Imagery. Remote Sensing of Environment,
Schwarz. (1978). Estimating the dimension of a model. Annals of Statistics.
Shibata. (1980). Asymptotically Eﬃcient Selection of the Order of the Model for Estimating Parameters of a Linear Process. Annals of Statistics,
Sims. (1980). Macroeconomics and Reality. Econometrica.
Sims, & Zha. (2006). Were There Regime Switches in Us Monetary Policy. American Economic Review 96, 54 81.
Tewari, Mehlhorn, Parrott, & Hill. (2015). Climatic Variability and Crop Price Trends in West Tennessee: A Bivariate Granger Causality Analysis. European Scientific Journal.
Tsay, R. S. (2013). Multivariate Time Series Analysis: With R and Financial Applications. John Wiley & Sons.
Zhang, Vedenov, & Wetzstein. (2007). Can the US Ethanol Industry Compete in the Alternative Fuels Market? Agricultural Economics. Fuels market? Agricultural Economics.