In order to make the data “talk,” economists utilize a range of statistical methods that vary from highly complex models to a simple display of historical data. It is generally held that by means of statistical correlations one can organize historical data into a useful body of information, which in turn can serve as the basis for assessments of the state of the economy. It is held that through the application of statistical methods on historical data, one can extract the facts of reality regarding the state of the economy.

Unfortunately, things are not as straightforward as they seem to be. For instance, it has been observed that declines in the unemployment rate are associated with a general rise in the prices of goods and services. Should we then conclude that declines in unemployment are a major trigger of price inflation? To confuse the issue further, it has also been observed that price inflation is well correlated with changes in money supply. Also, it has been established that changes in wages display a very high correlation with price inflation.

So what are we to make out of all this? We are confronted here not with one, but with three competing “theories” of inflation. How are we to decide which is the right theory? According to the popular way of thinking, the criterion for the selection of a theory should be its predictive power. On this Milton Friedman wrote,

The ultimate goal of a positive science is the development of a theory or hypothesis that yields valid and meaningful (i.e., not truistic) predictions about phenomena not yet observed.1

So long as the model (theory) “works,” it is regarded as a valid framework as far as the assessment of an economy is concerned. Once the model (theory) breaks down, we look for a new model (theory). For instance, an economist forms a view that consumer outlays on goods and services are determined by disposable income. Once this view is validated by means of statistical methods, it is employed as a tool in assessments of the future direction of consumer spending. If the model fails to produce accurate forecasts, it is either replaced, or modified by adding some other explanatory variables.