I. Introduction
Problem of multicollinearity arising from the nature of economic models and data often result in highly unstable and unrealistic estimates of structural parameters, especially if one is restricted to
unbiased estimators such as OLS. If this problem is resolved by deleting important variables from the models, one risks a serious omitted-variables-specification bias Brown and Beattie, 1975. Ridge
regression has been known and developed as an alternative prediction method to ordinary least squares regression OLS in cases where there is a high degree of multicollinearity among the
predictor variables. If the mean square error MSE criterion is used as a measure of accuracy, there always exists a more accurate “ridge regression” estimate than the unbiased OLS estimate, as shown
by Hoerl and Kennard 1970a, pp.61-63. Estimating Goldfeld’s conventional money demand function is an area where there is a high degree of overlap among the predictor variables and should
thus be an excellent area to apply ridge regression instead of the usual OLS regression used Tracey and Sedlacek, 1983. It has long been recognized that multicollinearity does not hinder and
sometimes aids in forecasting as long as the multicollinearity remains in the forecast period. However, in a demand for money equation with several interest rate, a changing term structure may
alter the multicollinearity pattern. In such circumstances, ridge estimates which are less affected by multicollinearity may provide better forecasts than OLS estimates Watson and White, 1976.
Obtaining valid prediction equations in this area is often difficult because the high degree of multicollinearity tends to create very different prediction equations from year to year. Thus the
process of validating these equations typically involves collecting very large samples over many years. Ridge regression has been found to be most effective in exactly these cases. Ridge regression
should result in more stable equations with high multicollinear data, and thus, should be more valid using smaller samples than typically required by least squares method.
The purpose of this study was to examine the efficacy of ridge regression over ordinary least squares regression in Goldfeld’s conventional money demand function. This study has two aims : 1.
To propose a test of the ability of the ridge regression to forecast. 2 To show that the coefficients of the ridge regression statistically are not different as the OLS coefficients.
II. Literature Review
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