Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques

Hilda Chepkosgei Rotich; Joel Cheruiyot Chelule; Herbert Imboga

doi:doi:10.11648/j.ajtas.20200906.15

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Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques

Hilda Chepkosgei Rotich, Joel Cheruiyot Chelule, Herbert Imboga

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 6)

Received: 29 October 2020 Accepted: 10 November 2020 Published: 27 November 2020

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Abstract

Favorable product prices act as a set off for any commercial activity normally and perhaps its sustainability. Tea is a significant commercial crop in Kenya and has contributed immensely to the economy’s Gross Domestic Product (GDP) through its export earnings. However, tea industry has gone through many phases of ups and downs, particularly in terms of its export performance recently. Therefore, there is need to study the behavior of tea auction prices to get a deeper perspective into the behavior of the tea prices and to develop a model that is suitable to forecast the tea auction prices precisely. The study aimed at analyzing the trend of tea auction prices in Kenya, fit a suitable model for forecasting tea auction prices and finally, forecast the future tea prices using the most optimal model. The findings of this study will therefore inform the government and other policy makers in terms of its policy formulation regarding the tea sector in order to accord it a competitive position in the global arena. The study used univariate and multivariate forecasting techniques which do not impose co-integration restrictions such as the Autoregressive Integrated Moving Average (ARIMA) and the Vector Autoregressive (VAR). The techniques used were chosen because of their flexibility, wide acceptability and they are also easy to utilize. The study used secondary data for the Mombasa Tea Auction Centre for a period of 2009 to 2018. Augmented Dicker-Fuller (ADF) test was performed for unit root tests to check for stationary of the price series. ACF and PACF functions were estimated to assist in deciding the most appropriate orders of AR (p) and MA (q) models. AIC test was used because were considering several ARIMA models and the model with the lowest AIC was chosen. VAR model showed a high forecasting error with MAPE of 85.64% compared to that ARIMA of 9.2% for tea prices. ARIMA model performed much better than VAR model because of its high forecasting accuracy.

Published in	American Journal of Theoretical and Applied Statistics (Volume 9, Issue 6)
DOI	10.11648/j.ajtas.20200906.15
Page(s)	296-305
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), 2020. Published by Science Publishing Group

Keywords

Analysis, Tea, Prices, Autoregressive Integrated Moving Average Model (ARIMA), Vector Auto Regression (VAR)

References

[1]	Hettiarachchi, H., & Banneheka, B. (2012). Time Series Regression and Artificial neural network approaches for forecasting unit price of tea at Colombo auction. Journal of the national science foundation of Sri Lanka, 41 (1).
[2]	Dilshani, Chandima & Samithree (2016). Forecasting black tea action prices by capturing common seasonal patterns. Sri Lankan Journal of Applied Statistics 16 (3) 195.
[3]	Hewapathirana, I. U. and Tilakaratne C. D. (2012). Modeling and Forecasting Sri Lankan black tea prices exploring temporal patterns, Proceedings of the ISM International Statistical Conference 2012, Johor Bharu, Malaysia.
[4]	Induruwage, D., Tilakaratne, C., & Rajapaksha, S. (2016). Forecasting Black Tea Auction Prices by Capturing Common Seasonal Patterns. Sri Lankan Journal of Applied Statistics, 16 (3): 195-214.
[5]	Box, G. E and Jenkins, G. M (1976). Time Series Analysis: Forecasting and Control san Francisco. Calif: Holden-Day.
[6]	Krishnarani, S. D. (2013). Time series outlier analysis of tea price data American Journal of Theoretical and Applied Statistics, 2 (1), 1-6.
[7]	Liu, H., & Shao, S. (2016,). India's Tea Price Analysis Based on ARMA Model. Modern Economy, 7 (2): 118-123.
[8]	Chaundry, S., Negi, Y., and Shukla, R. K. (2017). A time series analysis of auction prices of Indian tea. International Journal of Research in Economics and Social Sciences 7 (6): 100-111
[9]	N. A. M. R Senaviratna (2016). Forecasting Tea auction Prices in Sri Lanka:Box Jenkins approach; Indian Journal of Applied Research 6 (11): 722 -724.
[10]	Sims, c.a. (1980) Macroeconomics and Reality. Econometrica.
[11]	Dharmasena, K. A. S. D. B. (2004). International black tea market integration and price discovery (PHD thesis), Texas A & M University.
[12]	Zivot, E. and Wang J. (2006), Unit root tests. Modelling Financial Time Series with S-PLUS, Springer, pages 111-139.
[13]	Hettiarachchi, H. A. C. K. and Banneheka B. M. S. G. (2012). Time Series Regression and Artificial Neural Network Approaches for Forecasting Unit Price of Tea at Colombo Auction. Journal of the National Science Foundation Sri Lanka, 41: 35-40.
[14]	Dharmasena, K. A. S. D. B. and Bessler, D. A. (2004). Weak Form Efficiency Vs Semi Strong Form Efficiency in Price Discovery: An Application to International Black Tea Markets, Sri Lankan Journal of Agricultural Economics, 6 (1), 2004.

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APA Style

Hilda Chepkosgei Rotich, Joel Cheruiyot Chelule, Herbert Imboga. (2020). Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques. American Journal of Theoretical and Applied Statistics, 9(6), 296-305. https://doi.org/10.11648/j.ajtas.20200906.15

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ACS Style

Hilda Chepkosgei Rotich; Joel Cheruiyot Chelule; Herbert Imboga. Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques. Am. J. Theor. Appl. Stat. 2020, 9(6), 296-305. doi: 10.11648/j.ajtas.20200906.15

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AMA Style

Hilda Chepkosgei Rotich, Joel Cheruiyot Chelule, Herbert Imboga. Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques. Am J Theor Appl Stat. 2020;9(6):296-305. doi: 10.11648/j.ajtas.20200906.15

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@article{10.11648/j.ajtas.20200906.15,
  author = {Hilda Chepkosgei Rotich and Joel Cheruiyot Chelule and Herbert Imboga},
  title = {Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {6},
  pages = {296-305},
  doi = {10.11648/j.ajtas.20200906.15},
  url = {https://doi.org/10.11648/j.ajtas.20200906.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200906.15},
  abstract = {Favorable product prices act as a set off for any commercial activity normally and perhaps its sustainability. Tea is a significant commercial crop in Kenya and has contributed immensely to the economy’s Gross Domestic Product (GDP) through its export earnings. However, tea industry has gone through many phases of ups and downs, particularly in terms of its export performance recently. Therefore, there is need to study the behavior of tea auction prices to get a deeper perspective into the behavior of the tea prices and to develop a model that is suitable to forecast the tea auction prices precisely. The study aimed at analyzing the trend of tea auction prices in Kenya, fit a suitable model for forecasting tea auction prices and finally, forecast the future tea prices using the most optimal model. The findings of this study will therefore inform the government and other policy makers in terms of its policy formulation regarding the tea sector in order to accord it a competitive position in the global arena. The study used univariate and multivariate forecasting techniques which do not impose co-integration restrictions such as the Autoregressive Integrated Moving Average (ARIMA) and the Vector Autoregressive (VAR). The techniques used were chosen because of their flexibility, wide acceptability and they are also easy to utilize. The study used secondary data for the Mombasa Tea Auction Centre for a period of 2009 to 2018. Augmented Dicker-Fuller (ADF) test was performed for unit root tests to check for stationary of the price series. ACF and PACF functions were estimated to assist in deciding the most appropriate orders of AR (p) and MA (q) models. AIC test was used because were considering several ARIMA models and the model with the lowest AIC was chosen. VAR model showed a high forecasting error with MAPE of 85.64% compared to that ARIMA of 9.2% for tea prices. ARIMA model performed much better than VAR model because of its high forecasting accuracy.},
 year = {2020}
}

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TY - JOUR
T1 - Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques
AU - Hilda Chepkosgei Rotich
AU - Joel Cheruiyot Chelule
AU - Herbert Imboga
Y1 - 2020/11/27
PY - 2020
N1 - https://doi.org/10.11648/j.ajtas.20200906.15
DO - 10.11648/j.ajtas.20200906.15
T2 - American Journal of Theoretical and Applied Statistics
JF - American Journal of Theoretical and Applied Statistics
JO - American Journal of Theoretical and Applied Statistics
SP - 296
EP - 305
PB - Science Publishing Group
SN - 2326-9006
UR - https://doi.org/10.11648/j.ajtas.20200906.15
AB - Favorable product prices act as a set off for any commercial activity normally and perhaps its sustainability. Tea is a significant commercial crop in Kenya and has contributed immensely to the economy’s Gross Domestic Product (GDP) through its export earnings. However, tea industry has gone through many phases of ups and downs, particularly in terms of its export performance recently. Therefore, there is need to study the behavior of tea auction prices to get a deeper perspective into the behavior of the tea prices and to develop a model that is suitable to forecast the tea auction prices precisely. The study aimed at analyzing the trend of tea auction prices in Kenya, fit a suitable model for forecasting tea auction prices and finally, forecast the future tea prices using the most optimal model. The findings of this study will therefore inform the government and other policy makers in terms of its policy formulation regarding the tea sector in order to accord it a competitive position in the global arena. The study used univariate and multivariate forecasting techniques which do not impose co-integration restrictions such as the Autoregressive Integrated Moving Average (ARIMA) and the Vector Autoregressive (VAR). The techniques used were chosen because of their flexibility, wide acceptability and they are also easy to utilize. The study used secondary data for the Mombasa Tea Auction Centre for a period of 2009 to 2018. Augmented Dicker-Fuller (ADF) test was performed for unit root tests to check for stationary of the price series. ACF and PACF functions were estimated to assist in deciding the most appropriate orders of AR (p) and MA (q) models. AIC test was used because were considering several ARIMA models and the model with the lowest AIC was chosen. VAR model showed a high forecasting error with MAPE of 85.64% compared to that ARIMA of 9.2% for tea prices. ARIMA model performed much better than VAR model because of its high forecasting accuracy.
VL - 9
IS - 6
ER -

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Author Information

Hilda Chepkosgei Rotich

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Joel Cheruiyot Chelule

Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Science, Nairobi, Kenya
Herbert Imboga

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

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APA Style

Hilda Chepkosgei Rotich, Joel Cheruiyot Chelule, Herbert Imboga. (2020). Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques. American Journal of Theoretical and Applied Statistics, 9(6), 296-305. https://doi.org/10.11648/j.ajtas.20200906.15

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ACS Style

Hilda Chepkosgei Rotich; Joel Cheruiyot Chelule; Herbert Imboga. Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques. Am. J. Theor. Appl. Stat. 2020, 9(6), 296-305. doi: 10.11648/j.ajtas.20200906.15

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AMA Style

Hilda Chepkosgei Rotich, Joel Cheruiyot Chelule, Herbert Imboga. Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques. Am J Theor Appl Stat. 2020;9(6):296-305. doi: 10.11648/j.ajtas.20200906.15

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@article{10.11648/j.ajtas.20200906.15,
  author = {Hilda Chepkosgei Rotich and Joel Cheruiyot Chelule and Herbert Imboga},
  title = {Analysis of Tea Auction Prices Using Non-Cointegration Based Techniques},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {6},
  pages = {296-305},
  doi = {10.11648/j.ajtas.20200906.15},
  url = {https://doi.org/10.11648/j.ajtas.20200906.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200906.15},
  abstract = {Favorable product prices act as a set off for any commercial activity normally and perhaps its sustainability. Tea is a significant commercial crop in Kenya and has contributed immensely to the economy’s Gross Domestic Product (GDP) through its export earnings. However, tea industry has gone through many phases of ups and downs, particularly in terms of its export performance recently. Therefore, there is need to study the behavior of tea auction prices to get a deeper perspective into the behavior of the tea prices and to develop a model that is suitable to forecast the tea auction prices precisely. The study aimed at analyzing the trend of tea auction prices in Kenya, fit a suitable model for forecasting tea auction prices and finally, forecast the future tea prices using the most optimal model. The findings of this study will therefore inform the government and other policy makers in terms of its policy formulation regarding the tea sector in order to accord it a competitive position in the global arena. The study used univariate and multivariate forecasting techniques which do not impose co-integration restrictions such as the Autoregressive Integrated Moving Average (ARIMA) and the Vector Autoregressive (VAR). The techniques used were chosen because of their flexibility, wide acceptability and they are also easy to utilize. The study used secondary data for the Mombasa Tea Auction Centre for a period of 2009 to 2018. Augmented Dicker-Fuller (ADF) test was performed for unit root tests to check for stationary of the price series. ACF and PACF functions were estimated to assist in deciding the most appropriate orders of AR (p) and MA (q) models. AIC test was used because were considering several ARIMA models and the model with the lowest AIC was chosen. VAR model showed a high forecasting error with MAPE of 85.64% compared to that ARIMA of 9.2% for tea prices. ARIMA model performed much better than VAR model because of its high forecasting accuracy.},
 year = {2020}
}

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