In times series regression for short run analysis, the assumption that initial values of a variable have influence on current values of the same variable does hold. Such an assumption guides the economic planners in policy making and evaluations.
It is possible that money demand in the current period has a link to money demand in previous periods of time. This is the usual assumption in the short-run. In macroeconomics it becomes worthwhile if such an assumption can be subjected to empirical testing to see how it turns out.
Elementary or Advanced knowledge of Cointegration is required before running short run tests because the detection of Cointegration in the error term or residuals is a precondition for Short Run Models to be run. Short Run Models are also known as Error Correction Models.
The implication being that the role monetary policy plays on demand for money may be felt in the next economic period. It could also mean that monetary policy can take up to one year to take effect in the economy. This remains a discussion for macroeconomics research papers.
►The nominal short run demand for money relationship expressed in functional form:
In the discussion on long run demand for money, the equation below was derived representing demand for real cash balances. The model is now stated thus:
As shown in functional notation, in the short-run real money demand is lagged in an attempt to express a relationship between real cash balances in the current period and real cash balances in the previous period. The result is the model now specified.
is real cash balances in the current period
is real cash balances in the lagged period
is the short-run real money demand at zero income level
is the short-run interest elasticity of demand
is the short-run income elasticity of demand
is the short-run rate of change for real cash balances in the lagged period
is the current interest rate on bond/market interest rate.
is the gross output or product
is the stochastic variable
Granger, C.W.J. (1986): “Developments in the study of co-integrated Variables”, Oxford University Press, England.
Gujarati, D. N. (2003): Basic Econometrics, 4th ed., McGraw-Hill Higher education, Newyork.
Hendry, D.F. (1986): “Econometric modelling with co-integrated variables: An Overview, Oxford University Press, England.