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.
The tentative nature of theories implies that our knowledge of the real world is elusive.
Since it is not possible to establish “how things really work,” then it does not really matter what the underlying assumptions of a model are. In fact anything goes, as long as the model can yield good predictions. According to Friedman,
The relevant question to ask about the assumptions of a theory is not whether they are descriptively realistic, for they never are, but whether they are sufficiently good approximation for the purpose in hand. And this question can be answered only by seeing whether the theory works, which means whether it yields sufficiently accurate predictions.2
Why the Predictive Capability for Accepting a Model Is Questionable
The popular view that sets predictive capability as the criterion for accepting a model is questionable. Even the natural sciences, which mainstream economics tries to emulate, don’t validate their models this way. For instance, a theory that is employed to build a rocket stipulates certain conditions that must prevail for its successful launch.
One of the conditions is good weather. Would we then judge the quality of a rocket propulsion theory on the basis of whether it can accurately predict the date of the launch of the rocket? The prediction that the launch will take place on a particular date in the future will only be realized if all the stipulated conditions hold.
Whether this will be so cannot be known in advance. For instance, on the planned day of the launch it may be raining. All that the theory of rocket propulsion can tell us is that if all the necessary conditions will hold, then the launch of the rocket will be successful. The quality of the theory, however, is not tainted by an inability to make an accurate prediction of the date of the launch.
The same logic also applies in economics. We can say confidently that, all other things being equal, an increase in the demand for bread will raise its price. This conclusion is true, and not tentative. Will the price of bread go up tomorrow, or sometime in the future? This cannot be established by the theory of supply and demand. Should we then dismiss this theory as useless because it cannot predict the future price of bread?
Or consider a situation when a stock market is following an “up” trend over several years. As a result, an analyst has established that it is possible to outperform the stock market by following the barking of a dog.
If the dog barks three times it is a buy and if he barks once it is a sell. Should such a framework be accepted as a valid theory because it makes good forecasts?
Contrary to the popular way of thinking the criteria for selecting a model is not how well it worked in the past — i.e. passed the criteria of backtesting and a life test — but whether it is theoretically sound.
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