CHAPTER THREE
RESEARCH METHODOLOGY
3.1 INTRODUCTION:
RESEARCH DESIGN
In the course of this research, the
researcher employs regression analysis based on the classical linear regression
model, otherwise known as ordinary least square (OLS) techniques is chosen by
the researcher.
The researcher’s choice of the technique is based not only by
its computational simplicity but also as a result of its optimal properties
i.e. BLUE properties (Best, linear, unbiased estimator), others includes
minimum variance, zero mean value of the random terms. (Koutsoyiannis, 2001 and
Gujarati, 2004).
3.2 MODEL
SPECIFICATION
In the quest of this
study, hypothesis has been stated with the view of ascertaining if money market
development has nay significant impact on the economic growth of Nigeria. The
model to be used is multiple linear regression model between the explanatory
variables and the endogenous variable.
The model is represented in a
functional form below:
GDP = F (TBILLS, INF,
M2, INT) ……………………………
3.1.1
Its statistical form
is as represented below;
GDP = bo + b1TBILLS + b2INF + b3M2
+ b4INT + Ut
…… 3.2.1
For research purpose the model, is
re – specified in its log form below;
Log GDP = bo + b1 log TBILLS + b2 log INF + b3 log M2
+ b4 log
INT + Ut ……………… 3.2.2
Where;
GDP = Nominal gross
domestic product (dependent variable)
TBILLS = Government
treasury bills (Independent variable)
INF = Inflation rate
(Independent variable)
M2 = Broad money supply (Independent variable)
INT = Money market interest rate
t = Time
from 1980 – 2011
bo = Constant
b1, b2, b3, b4 are the relative parameter or coefficient of the
independent variables.
3.3 SOURCES
OF DATA
· The data for this research project is obtained from
the following sources:
· Central bank of
Nigeria statistical bulletin for various years
· National Bureau
of statistics publication – Annual reports of various years.
· Central bank of
Nigeria economic and financial review for various years.
· Others includes;
textbooks, journals, magazines etc.
3.4
MODEL EVALUATION
Hence, the test that will been considered in this study include:
· Coefficient of
multiple determinant (R2)
· Standard error
test (S.E)
· T – test
· F – test
· Durbin – Watson
statistics
COEFFICIENT OF MULTIPLE DETERMINATIONS (R2): It is used to measure the proportion of variations in
the dependent variable which is explained by the explanatory variables. The
higher the (R2) the greater the proportion of the variation in the dependent
variable caused by changes in the independent variables.
STANDARD ERROR TEST (S.E): It is used to test for the reliability of the
coefficient estimates
DECISION RULE:
If S.E < 1/2bi, reject the null hypothesis and conclude that the
coefficient estimate of the parameter is statistically significant. Otherwise
accept the null hypothesis.
T – TEST: It
is used to test for statistical significance of individual estimate parameter.
In this research, T – test is chosen because the population variance is unknown
and the sample size is less than 30.
DECISION RULE
If T – cal > T –
tab, reject the null hypothesis and conclude that the regression coefficient is
statistically significant. Otherwise accept the null hypothesis.
DURBIN WATSON (DW): It is used to test for the presence of auto – correlation (serial
correlation).
DECISION RULE
If the computed Durbin Watson
statistics is less than the tabulated value of the lower limit there is
evidence of positive first order serial correlation. However, if it lies
between the upper limit, there is inconclusive evidence regarding the presence
or absence of positive first order serial correlation.
3.5
DATA DESCRIPTION AND TRANSFORMATION
The data obtained were
transformed into logarithm to obtain growth rates. Among such transformation were
GDP, TBILLS, INF, M2 and
INT, as gotten from CBN statistical bulletin. The choice of logging this data
is to further access their reliability and significance. E – View econometric
software is used to run this regression.