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Quantity Theory Of Money Essay

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CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Theoretical Literature
2.1.1 The Quantity Theory of money

The monetary policy is based on the several monetary theories. The supply of money which is a monetary policy is based on one of the quantity theory of money. One of these is Irving Fisher’s quantity theory of money, which states that the quantity of money is the main determinant of the price level or the value of money. As the quantity of money in circulation increases, the price level also increase in direct proportion. If the quantity of money is doubled, the price level will be doubled also. Fisher has explained his theory in terms of his equation of exchange. See formula bellow. PT = miv1 + m1v1.
Where P = price level …show more content…

2.2.6. Relationship between R2 and F- Test
(Domar 2013) opined that there is a relationship between the coefficient of determination R2 and the F-test. The larger the R2 the greater the F value, when R2 = 1, F is infinite. When R2 = 0, f is zero. R2 is a major component in the calculation of f test.

2.2.7 Functional Form Models
2.2.7.1 Linear Function
A linear function has one independent variable and one dependent variable. The linear function assumes an additive relationship, a linear function is not a good measure of economic optimum. Example of linear function is as follows:
Y = b0 + b1X1 + b2X2
The independent variables are Xs, and the dependent variable is
Y.b0 is constant, b1 is coefficient

2.2.7.2 Double log Function
The double log is also known as log-log. The log-log entails that both the dependent and independent variable has log component, it is stated as follows:-
Lny = 1nbo+b1 lnX1 + b2 lnx2
In the double log function bo; b1 represent elasticities and the sum of bo+b1 represent economies and diseconomies of scales. The double log is useful for long run …show more content…

The exponent functions are not very popular.
2.2.8 Stationarity (long run relationship)
If two variables are trending over time a regression of one on the other could have a high coefficient of multiple determinations (R2) even if the two variables are totally unrelated. If the regression model are not stationary (nonsationary), then it violent the asymptotic assumption or normal distribution, in such case understanding hypothesis test about the regression parameter will not be valid, it will produce a false result (Spurious results).
A stationary variable is a statistical properties that do not change with time.
Stationarity is a quality of a model or a process in which the statistical parameter (mean, variance, standard deviation) of process does not change with time (Challis and Kitney 1991), the stationarity depend on lag alone, and does not change with time at which function was calculated. Stationarity is essential because time series data has peculiar characteristic unlike cross sectional data, there is always a problem of autocorrelation and multi-colinearity, in time series data. It is because of these peculiar characteristics that stationarity test is carried out on time series

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