INFLATION AND GOVERNMENT EXPENDITURE: RESEARCH DESIGN AND METHODOLOGY

The study deals with an empirical analysis on the relationship between inflation rate and government expenditure in Nigeria 1980-2009. It is purely a survey of inflationary trend along side with the level of government expenditure over the years to maintain stability in the Nigeria economy.

Model Specification

            By theoretical definition, ‘it is given that a country inflation rate is negatively related government expenditure. Therefore, the analytical model for this study can be stated to reflect that the rate of inflation (Ø) is a function of government expenditure (G).

That is Ø = F (G)

            For the purpose of this study. I assume the following linear model. A regression model is linear when the parameter of the model occurs linearly. Therefore from the functional relationship we can write an equation

Ø = b0 +bi G + U

Where

Ø = dependent variable – inflation rate

G = independent variable – Government expenditure

B0, b1 = coefficient of the parameter estimates of the regression line.

U = Error term

Sources of Data

            In carrying out this study, the research made use of secondary method of data collection. The data were gathered from public sources of some government agency and parastatals. They were data collected over the year and statistical bulletin and statistical abstract of federal office of statistic, the central bank of Nigeria (CBN). Data obtain cover the period 1980-2009 and mainly an index of government expenditure and inflation rate.

Procedure for Data Analysis

            In the regression analysis, tables and ordinary least squares were used in determining the relationship between the variables. The variable span through inflation rate and total government expenditure, which is employed in the model specified above.

Ø = b0 + bi G +U

Where

Ø = inflation rate

B0 = the intercept

B1 = parameter estimate

G = Government expenditure

U = Error term

            Tested above, the calculated variable of the parameter (b0 and bi) are used to divide the parameters in the regression model. This gives the t* – the computed values which is thus compared with the critical table values (t0.025)

(Cap) = computed value

t0.025 = critical table value

n = number of years

k = number of parameters,

Decision Rules                         

Which is thus compared with the critical table values (t 0.025)

(Cap) = computed value

t0.025 = critical table vaule

n = number of years

k = number of parameters,

Decision Rules 

            The study will reject H0 if the computed value (t* – values) greater than the critical table value (t0.025) and alternatively accept.

H1. that is, if t* > t0.025 accept H1 and reject H0. if otherwise the researcher reject H1 and accepts H0. That is, if t* < t0.25.