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), 2024. Published by Science Publishing Group |
Analysis, Tea, Prices, Autoregressive Integrated Moving Average Model (ARIMA), Vector Auto Regression (VAR)
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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
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
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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Download@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}
}
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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