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# Testing Money Demand Equation, Econometrics Assignment, SAS - Speech or Presentation Example

Summary
The money market equation is a link between a few essential macroeconomic factors which indicates the aggregate demand of money in an economy over a considerable period of time. However, the form of the equation is purely a general one, which need not hold in the context of each…

## Extract of sampleTesting Money Demand Equation, Econometrics Assignment, SAS

Download file to see previous pages... To interpret whether variations in dependent variable, (mt – pt) is explained or not, explanatory powers of each of the two independent variables have to be considered. If the Student’s-statistics of the respective estimated coefficients are found to be greater than the tabulated value at the given degrees of freedom, the corresponding variable is considered to be significantly explaining variations in the model and vice-versa.
At 107 degrees of freedom, tabulated t-statistic is 1.99, which is lower than the estimated values in either case. Hence, each one of the two variables is found to be significantly explaining variations in the dependent variable so that variation in the model is perfectly explained.
Money demand in excess of the general price level is found to be highly dependent on income and rate of interest in context of the US economy. The dependence is found to be in line with that of theory which says that demand for money is directly related to income but inversely related to the rate of interest. Moreover, the rate of interest in the nation is also gradually falling over time, revealing that the money demand in the nation is rising actually. A rise in money demand is actually a positive sign for economies which had been engulfed in a recession, since that implies a rise in aggregate demand and thus rise in national income. Hence it could be said that the US economy is actually at the verge of experiencing boom. In fact, a rising income will attract investors from all over the world thus ensure the nation a consistent period of boom.
Since the number of observations is the same as that in the previous case, the degrees of freedom are equal to 107. So, using the rule mentioned above it can be said that both the intercept and time factor can explain variations in the dependent variable significantly.
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