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
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
 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.
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).
            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.   
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