Modelling Tea Production in Kenya using the Linear Regression with GARCH error term
Main Article Content
Abstract
Tea is a popular drink globally and contributes to 4% of Kenya’s annual GDP. The continued variation
in Tea production has prompted numerous research. Some researchers have linked the variation to climatic changes while others to soil fertility dynamics. In the present research, we link the tea production changes to climatic variables. Thus, we attempt to establish the linear regression equation modeling relationship between trends of tea production and climatic variables, namely NDVI, minimum humidity, maximum humidity, rainfall, minimum temperature, maximum temperature, and solar radiation between January 2007 and December 2015. We have also presented the effect of volatility and seasonality of climatic variables on tea production in Kenya. The effect of volatility and seasonality is investigated via the GARCH model, which is used to estimate linear regression error terms for tea production using data from major tea zones in Kenya, namely Embu, Kakamega,
Kericho, Kisii, Meru, and Nyeri. The demonstration in the research via the already established GARCH model is based on a combination of garchorder(2,2) and armaorder(1,1) that seasonality affects tea production with climatic variables as independent variables. We compared the analysis results among the data obtained from different major tea zones in Kenya. The results summarized in tables show that in the presence of seasonality larger error term exists than in the absence of seasonality for most tea zones. The AIC and BIC in most of the tables are lower in cases of deseasonalized data. The loglikelihood in most cases is also higher in cases on deseasonalized data. Comparison of deviation between tea production value estimated using data and constructed tea production based
on linear regression equation shows that the seasonal data exhibits higher deviation than deseasoned data in most cases. The effect of seasonality on serial correlation is also evident. There exists serial correlation for most cases of deseasoned data compared to when the data has seasonality. A future study may consider estimating linear regression error terms amidst volatility effect on seasonal data for different GARCH models
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
