Often, we observe that two pieces of data, which are not supposed to have any relationship, appear to have a very high correlation. What makes the apparent good correlation is that they both exhibit an upward long-term trend. In addition, fluctuations of the data do not seem to converge around the trend but just seem to move in an upward direction. These types of data statisticians label as “non-stationary.”
In contrast, data that converges around a fixed value is labeled “stationary.” Stationary data implies an unchanged structure, something that is stable. Now, if something drifts aimlessly, it is not possible to say much about its future course. Consequently, utilizing non-stationary data in economic analyses is likely to produce misleading results.
For instance, an economist wants to establish how changes in production affect consumption. The common procedure for this is to apply statistical methods on consumption and production data in order to establish their interrelationship. By means of a statistical technique, also known as regression analysis, one establishes how consumption and production are quantitatively connected to each other. Let us say that the economist has established that the relationship between consumption and production is depicted by the following mathematical expression:
Consumption = 10 + 0.5*Production
Armed with this finding, the economist can now tell us the amount of consumption for a given production. Thus, if production is 100 then consumption will be 60 (because 10+0.5*100=60). The numbers 10 and 0.5, which were generated by a regression method, are the estimates of the true parameters in the real world, or so it is held.
It is maintained that, on average, these estimates are a very close approximation of the true parameters. It is also held that any conclusions derived from the equation regarding the relationship between consumption and production is a reflection of reality, as long as the equation’s performance in terms of its forecasting capability is good.
The 2003 Nobel Laureate in economics, Clive Granger, contests this. He argues that no meaningful conclusions can be drawn from the above equation if the data employed in establishing it is non-stationary. Granger is of the view that most data employed by economists is non-stationary. The parameter estimates that one obtains from such data is likely to be misleading and hence the outcome of the analysis is likely to be meaningless. So how does one overcome the problem?
If one were to establish a common factor that influences both consumption and production, then these two time-series are said to be connected or co-integrated. Granger and others have shown, by means of quantitative methods, that the introduction of a common factor transforms the non-stationary time series into stationary.
This common or co-integrating factor between consumption and production could be that, without production there cannot be consumption and, without consumption, production is not required. Another example is an identical good, which is trading in different locations. The day-to-day fluctuations in prices may appear to be random in various locations and therefore most likely will not correspond to each other. However, the existence of arbitrage and the law of supply and demand will make sure that over time prices in various locations will move close to each other.
Instead of trying to find out what the co-integrating factor is, Granger and others have produced a mechanized framework, which enables economists to establish whether the data complies with co-integration. Once the data is co-integrated, it could be employed to establish the parameters estimate, which will be valid, or so it is held (pp. 255-278).
Various statistical results that are produced by means of the Granger’s framework therefore are regarded as valid since they have been applied on co-integrated data. Granger’s criticism raises serious doubts about past conclusions regarding economic interrelationships, which were reached by means of the old techniques.
Are There Constants in Economics?
The major issue that Granger did not address is not whether the old techniques have been generating valid parameters estimates, but whether such parameters exist at all. In the natural sciences, the employment of mathematics enables scientists to formulate the essential nature of objects. Consequently, within given conditions, the same response will be obtained repeatedly. The same approach, however, is not valid in economics. Economics deals with acting human beings and not mere objects. According to Mises,
The experience with which the sciences of human action have to deal is always an experience of complex phenomena. No laboratory experiments can be performed with regard to human action.
Individuals have the freedom of choice to change their minds and pursue actions that are contrary to what was observed in the past. Because of the unique nature of human beings, analyses in economics can only be qualitative. There are no quantitative parameters in the human universe. Thus, Mises wrote, “There are, in the field of economics, no constant relations, and consequently no measurement is possible.” Thus, while the laws of economics do not change, the subject of economics—human action in a changing world—is constantly changing.
The popular view that human activity can be captured by a mathematical formula expressed through fixed parameters implies that human beings are operating like machines. At best, mathematical formulations can be seen as a technique to provide a snapshot at a given point in time of various economic data. In this sense, it can be seen as a particular way of presenting historical data. These types of presentations, however, can tell us nothing about the driving causes of human economic activity. What’s more, the employment of established historical relations to assess the impact of changes in government policies will produce misleading results, notwithstanding Granger’s framework.
After all, to assume that a change in a government policy will leave the structure of the equations intact would mean that individuals in the economy ceased to be alive and were, in fact, frozen. In this regard Mises wrote,
As a method of economic analysis econometrics is a childish play with figures that does not contribute anything to the elucidation of the problems of economic reality.
Causality cannot be ascertained by means of mathematical methods but by means of understanding. This, in turn, can be done by a framework of thinking based on a non-refutable axiom that human beings use means to attain ends. With the help of this approach, one could establish that causality emanates from humans themselves and not outside factors.
There are no constant standards for measuring the minds, values, and ideas of men. Valuation is the means by which a conscious purposeful individual assesses the given facts of reality. Once the individual establishes what the facts are, he then assesses the most suitable means to attain his various ends. Individual goals or ends set the standard for valuing individuals.
Summary and Conclusions
The key nature of human beings is that they use their minds to evaluate the world around them, then they act purposefully to use means to attain ends. The usage of the mind, however, is not set to follow an automatic procedure, but rather every individual employs his mind in accordance with his own circumstances. This makes it impossible to capture human nature by means of a mathematical formula, as is done in the natural sciences. People have the freedom of choice to change their minds and pursue actions that are contrary to what was observed in the past. Because of the unique nature of human beings, analyses in economics can only be qualitative.
