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Modelling Time Series Analysis of Tea Production in Kenya from 1963 to 2015
George Kingori Maina
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Geisteswissenschaften, Kunst, Musik / Regional- und Ländergeschichte
Beschreibung
Research Paper (postgraduate) from the year 2022 in the subject History - Africa, grade: 3.5/4, , course: Social Science in Statistics, language: English, abstract: Tea is the leading export cash crop and a highly consumed beverage in Kenya. Small scale farmers are more than large scale farmers in Kenya. However, they own small sizes of land which is a limiting factor to tea production. Analysis of trends is an aspect of technical analysis that tries to predict the future movement of stock based on past data. The main objectives of this study is To construct a suitable time series model for the data, To determine the correlation between production and size of land, To forecast tea production Examples of analysis of trends are total monthly sales receipts in a departmental store and total monthly production by company. This research project was on the trends of tea production and area under tea that are collected annually since 1963 to 2015 and to construct a time series model of a suitable order for the process. The large scale size of land mean is 34882.04 hectares and the average small scale size of land is 4563.25 hectares .For the stationarity of the data the Dickey-Fuller test (ADF); Dickey-Fuller = -1.9254, Lag order = 3, p-value = 0.6045.The correlation of the large scale and small scale holders is; 0.9588537 and 0.9339925 hence strong linear relationship . The best possible models for modelling large scale is ARIMA (2, 1, 0) with Akaike Information Criterion (AIC) of 1885.73 and for Small scale farmers it ARIM(1,1,0) with AIC of 1915.76. The rate of change of the predicted tea production is 0.97 which is a very low rate. These values show that in the next 20 years there will be no significant changes in tea production in Kenya.
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MA, Correlation, ARIMA, Simple linear regresssion, Normality, Stationality, ACF, AR Box Jekinns Approach, PACF