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.
Table of Contents
1.0 INTRODUCTION
1.1 BACKGROUND OF STUDY
1.2 STATEMENT OF THE PROBLEM
1.3 OBJECTIVE OF THE STUDY
1.3.1 General objective
1.3.2 Specific objectives
1.4 RESEARCH QUESTIONS
1.5 JUSTIFICACTION
1.6 SIGNIFICANCE OF STUDY
1.7 ASSUMPTIONS OF THE STUDY
2.0 INTRODUCTION
2.1 THEORETICAL LITERATURE REVIEW
2.2 EMPIRICAL LITERATURE REVIEW
2.3 TIME SERIES MODELING REVIEW
3.0 INTRODUCTION
3.1 DATA
3.2 RESEARCH DESIGN
3.3 Autoregressive model (AR)
3.4 Moving average process (MA)
3.5 Autoregressive moving average (ARMA)
3.6 Autoregressive integrated moving average (ARIMA)
3.7 Box-Jenkins approach.
3.7.1 Autocorrelation function. (ACF)
3.7.2 Partial autoregressive function (PACF)
3.8 SEASONALITY
3.8.1 Multiple seasonal adjustment
3.8.2 Additive seasonal adjustment
4.1 Introduction
4.2 Descriptive statistics
4.3 Correlation
4.4 Trend
4.5 Stationarity
4.6 BUILDING BOX JENKINS APPROACH FOR MODELLING TOTAL TEA PRODUCTION.
4.6.1 Identification process
4.6.2 Estimation
4.6.3 Diagnostic Checks.
4.6.4 Forecast
5.1 INTRODUCTION
5.2 SUMMARY OF FINDINGS AND RESULTS
5.3CONCLUSION
5.4 RECOMMENDATIONS
Research Objective and Scope
This study aims to analyze the historical trends of tea production in Kenya from 1963 to 2015, evaluating the impact of land size on output through time series modeling to provide production forecasts.
- Analysis of tea production trends over a 52-year period.
- Examination of the correlation between land size and tea yield for both small and large-scale farmers.
- Application of the Box-Jenkins methodology for time series modeling (ARIMA).
- Forecasting of future tea production levels in Kenya based on existing historical data.
Excerpt from the Book
3.7 Box-Jenkins approach.
This approach was advocated by George Box and Gwilym Jenkins in the 1970 in the textbook time series analysis. It’s assumption is that the process that generates the time series can be estimated using an ARMA model if it is stationary or an ARIMA model if it is not stationary .It’s steps are as follows;
(i) Identification
In this step data helps select a subclass of the model that best summarizes the data. The step is fragmented into 2;
• Access whether the time series is stationary or not by use of the Augmented Dickey-Fuller test. If not, how many differences make it stationary?
This is written as ADF () in R.
• Identify the parameters of an ARMA model for the data. There are two plots that are used to help choose the p and parameters of the ARMA model. They are;
1. Autocorrelation function.
2. Partial autocorrelation function.
Summary of Chapters
1.0 INTRODUCTION: This chapter introduces the study's background, problem statement, objectives, and significance regarding tea production in Kenya.
2.0 INTRODUCTION: This chapter reviews theoretical and empirical literature related to global tea production and the methodological basis for time series modeling.
3.0 INTRODUCTION: This chapter details the research methodology, specifically the application of the Box-Jenkins approach, ARIMA modeling, and data collection sources.
4.1 Introduction: This chapter presents the descriptive statistics and technical findings derived from the analysis of tea production and land size data.
5.1 INTRODUCTION: This chapter provides a summary of the research findings, offers conclusions on the study objectives, and makes recommendations for stakeholders.
Keywords
Tea production, Kenya, Time series analysis, ARIMA model, Box-Jenkins method, Small scale farmers, Large scale farmers, Land size, Forecasting, Stationarity, Autocorrelation, Correlation, Agricultural economics, Trend analysis, Statistical modeling.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on modeling the time series of Kenyan tea production from 1963 to 2015 to identify trends and predict future yields.
What are the primary themes addressed?
The central themes include the agricultural performance of small versus large-scale tea farmers, the impact of land availability on production, and the application of statistical forecasting models.
What is the main objective of the study?
The main objective is to analyze historical tea production trends, determine the correlation between land size and output, and construct a suitable time series model to forecast future production.
Which scientific method is utilized?
The study employs the Box-Jenkins methodology, specifically using ARIMA (Autoregressive Integrated Moving Average) models to analyze the secondary data.
What content is covered in the main section?
The main section covers literature reviews, the formulation of time series models, the analysis of stationarity, the execution of the Box-Jenkins approach, and the presentation of empirical findings.
Which keywords characterize this work?
Key terms include Time series analysis, ARIMA, tea production, Kenya, land size, and statistical forecasting.
Why is the ARIMA(2, 1, 0) model chosen for total tea production?
The ARIMA(2, 1, 0) model was selected as the best fit after comparing various models based on the Akaike Information Criterion (AIC), as it provided the smallest value.
How does land size impact tea production according to the findings?
The analysis shows a strong linear correlation between land size and tea production, concluding that the availability of land is a significant factor affecting total output in Kenya.
What do the forecasts suggest for the next 20 years?
The study indicates that with current production factors, there will be no significant changes in tea production over the next two decades, as the rate of change is very low.
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- George Kingori Maina (Autor:in), 2022, Modelling Time Series Analysis of Tea Production in Kenya from 1963 to 2015, München, GRIN Verlag, https://www.grin.com/document/1194455
