Research proposal sample

CHAPTER THREE

METHODOLOGY

3.0 Introduction

This chapter presents the methodology which consists of the research design, data types and source, tools of data collection, and data analysis.

3.1 Research design

This study adopted a cross sectional survey design. This design was preferred because it enabled collecting data in a short time (Creswell 2003 and Koul 2005).Quantitative approach was also used because of its flexibility to form multiple scale and indices focused on the same construct (Ahunja 2005).

3.2 Study population

The researcher used secondary data obtained from the Uganda dairy cooperation and World Bank Africa database for the period between 2014 and 2015.

3.4 Data type and sources

Source of data was from secondary sources, The main source of data for this study was from UBOS and Uganda dairy cooperation, Economic Development (MOFED), Department of National Accounts, UBOS,. In addition World Bank Africa database will be used. The data will be from 2014 to 2016. Secondary data was sourced because it yields more accurate information than obtained through primary data, and it was also cheaper.

3.5 Tools of data collection

The data was got by presenting an introduction letter given to me by the head of department Economics and Statistics. This was clearly present my purpose to the different organizations where Iam eligible to collect the necessary data for my analysis.

3.6 Data Analysis

The time series data was analyzed using regression analysis, correlation and forecasting.

This is also known as the Box-Jenkins model. This methodology will be used to forecast the milk production in Uganda (2014-2017) acase study of fresh diary company. The model is based on the assumption that the time series involved are stationary. Stationary will first be checked and if not found, the series will be differenced d times to make it stationary and then the Autoregressive Moving Average (ARMA) (p, q) will be applied. The ARIMA procedure provides a comprehensive set of tools for univariate time series model identification, parameter estimation, and forecasting, and it offers great flexibility in the kinds of ARIMA models that can be analyzed. The ARIMA procedure supports seasonal, subset, and factored ARIMA models; intervention or interrupted time series models; multiple regression analysis with ARMA errors; and rational transfer function models of any complexity. The Box-Jenkins methodology has four steps that will be followed when forecasting milk production as below;

Identification.0 This involved finding out the values of p, d, and q

where;

p is the number of autoregressive terms

d is the number of times the series is differenced

q is the number of moving average terms

Analytical Procedure

This study used monthly data to examine the determinants of milk production in Uganda. The co-integration procedure requires time series in the system to be non-stationary in their levels. Moreover, it is imperative that all time series in the co-integrating equation have the same order of integration. Thus, the study first ascertained the time series properties of milk production and other explanatory variables by using the augmented Dickey-Fuller (ADF) and Philips-Perron test for stationarity (Dickey and Fuller, 1979 and 1981). The equation estimated for the ADF test is stated as follows:

The null hypothesis is that the series contains a unit root which implies that β₁=0 the null hypothesis is rejected if β₁ is negative and statistically significant. To determine the long run relationship between milk production and explanatory variables, the Johansen co-integration procedure was used (Johansen and Juselius, 1990 and Johansen, 1991). The procedure involves the estimation of a VECM. The VECM used in the study is as follows:

Where, Yt is the dependent variable, Zt is the explanatory variables, Xt is exogenous variable, Yt-1 –θZt-1 is the error correction and D is represents the difference operator. Furthermore, ε represents the vector of white noise process. The VECM allows causality to emerge even if the coefficients of the lagged differences of the explanatory variable are not jointly significant.

(Granger, 1983; Engle and Granger, 1987; Miller and Russek, 1990; Miller, 1991; Dawit, 2003). In this study, an attempt is made to specify the coffee export supply function of Ethiopia following Alemayehu (2002) and UNCTAD (2005). The hypothesized variables in this study are rainfall, relative domestic price, labour employed in agriculture, real exchange rate, domestic interest rate, foreign capital inflow, capacity utilization rate, real income, and term of trade. All variables are in natural logarithmic forms and β’s are parameters to be estimated which are elasticities.

1.8  Limitations of the study

1. The researcher faced financial constraint in terms of transport, stationery, research assistants, printing and binding services during the research process.
2. The time available for the research was limited to balancing time between research and other responsibilities may be hectic.