6. Hypothesis Testing
Assuming that the fitted model is a reasonably good approximation of
reality, we have to develop suitable criteria to find out whether the esti-
mates obtained in, say, Eq. (I.3.3) are in accord with the expectations of the
theory that is being tested. According to “positive” economists like Milton
Friedman, a theory or hypothesis that is not verifiable by appeal to empiri-
cal evidence may not be admissible as a part of scientific enquiry.
13
As noted earlier, Keynes expected the MPC to be positive but less than 1.
In our example we found the MPC to be about 0.70. But before we accept
this finding as confirmation of Keynesian consumption theory, we must en-
quire whether this estimate is sufficiently below unity to convince us that
this is not a chance occurrence or peculiarity of the particular data we have
used. In other words, is 0.70
statistically less than 1?
If it is, it may support
Keynes’ theory.
Such confirmation or refutation of economic theories on the basis of
sample evidence is based on a branch of statistical theory known as
statis-
tical inference (hypothesis testing).
Throughout this book we shall see
how this inference process is actually conducted.
7. Forecasting or Prediction
If the chosen model does not refute the hypothesis or theory under consid-
eration, we may use it to predict the future value(s) of the dependent, or
forecast, variable
Y
on the basis of known or expected future value(s) of the
explanatory, or
predictor, variable
X
.
To illustrate, suppose we want to predict the mean consumption expen-
diture for 1997. The GDP value for 1997 was 7269.8 billion dollars.
14
Putting