According to mainstream thinking, economic slumps are caused by various shocks. This means that these slumps are caused by unexpected events, which by implication are not known beforehand. Obviously if causes behind various shocks cannot be known in advance, it makes sense to look at various symptoms of the emerging economic slump. Based on the symptoms, experts could introduce measures to prevent the economy from declining into an economic recession.
It is however, held that it is not always possible to establish the conditions of the economy by just inspecting the data as a whole. What is required is to break the data into its key components. This, it is argued, will enable the economist to identify the possible sources of the economic illness.
Components That Drive the Data
According to popular thinking, data that is observed over time—labelled as time series—is driven by four components, these are:
- The trend component
- The cyclical component
- The seasonal component
- The irregular component
It is accepted that over time the trend determines the general direction of the data. The cyclical component portrays fluctuations in the data due to the business cycle influence. The effect of seasons such as winter, spring, summer and autumn and various holidays is conveyed by the seasonal component. The irregular component shows various irregular events, such as the covid-19 pandemic, or political shocks. It is held that the interplay of these four components generates the overall data.
Popular thinking regards the cyclical component as the most important part of the data. It is held that the isolation of this component would enable analysts to unravel the mystery of the business cycle.
It is thought that in order to preempt the negative side effects of the business cycle on people’s well-being, it is important to establish the magnitude of the cyclical component on as short a duration basis as possible. Like any illness the earlier it is detected the better are the chances of combating the disease. Thus, once the central bank has identified the magnitude of the cyclical component it could offset the cyclical influence by means of a suitable monetary policy.
According to various statistical studies, monthly fluctuations of the data are dominated by the influence of the seasonal component of the data.1 As the time span increases, the importance of the cyclical component increases while the influence of the seasonal component diminishes.
The cyclical influence is more powerful in the quarterly data than in the monthly data. The trend, it is assumed, exerts a strong influence on a yearly basis while having a minor effect on the monthly variations of the data.
While the irregular factor can be very “wild,” the effect it produces is of a short duration. Consequently, the effect of a positive shock is offset by a negative shock.
It follows that in order to be able to observe the influence of the business cycle on a short-term basis all that is required is to remove the influence of the seasonal factor. The method of the removal however, must make sure that the cyclical component of the data is not affected in the process.
Removal of the Seasonal Component
Most economists consider the seasonal component of the data as known in advance. For example, every year people buy warm clothes before the arrival of the winter and not before the arrival of the summer. In addition, people follow similar patterns of behavior year after year before major holidays. Thus, people tend to spend a larger fraction of their incomes before Christmas.
The assumption that the seasonal component is the same year after year means that its removal will not distort the cyclical component. This in turn will permit an accurate assessment of the magnitude of the cyclical influence on the data.
By means of statistical methods, economists generate monthly estimates of the seasonal components of a data. Once these components are removed from the raw data, the data becomes seasonally adjusted.
Note that once the seasonal component is removed we are left with the cyclical, irregular and trend component. Since it is held that on a monthly basis, the importance of the trend component is insignificant; hence, the fluctuations in the seasonally adjusted data are likely to mirror the effect of the business cycle.
Currently most government statistical bureaus worldwide utilize the US government computer programs X-12 and X-13 to estimate the seasonal components of a data. By means of sophisticated moving averages, these programs generate estimates of the seasonal components.
The computer program then uses the obtained estimates to adjust the data for seasonality, i.e., to remove the seasonal component from the raw data. The designers of these seasonal adjustment computer programs have also attempted to address the issue of the constancy of the seasonal component by allowing this component to vary over time.
For example, the seasonal component for retail sales in December will not be of the same magnitude year after year but will rather vary. Furthermore, these programs are instructed to employ only stable seasonality in the seasonal adjustment procedure.
When a program “discovers” that the seasonal components over time are not stable, the raw data is left unadjusted. It would appear that by means of sophisticated statistical and mathematical methods these programs could generate realistic estimates of the seasonal influence on the data, which in turn permits the identification of the cyclical component.2
Note again that the strength of the cyclical component could determine the direction of the central bank policy, i.e., whether the central bank will tighten or loosen its interest rate stance.
Observe that the computer programs are based on mechanical procedure with not much of economic theory to back it up. If the data appears to be very choppy then a high degree of moving average is applied. Conversely, a lower moving average is employed for a lesser volatile data.
The early version of the government computer program started the process of identifying the seasonal influence on a data by employing a fifteen-term moving average. While the latter version employs a twelve-term moving average. In the process of calculating the seasonal components, the computer program produces estimates for the trend and cycle components using either a weighted nine-term moving average or, a weighted thirteen-term or twenty-three-term moving average.
What we have here is a mathematical “data torturing” to ascertain quantitatively the seasonal influence without much information regarding the true seasonal component—no one has ever observed the actual seasonal component. The process of subjecting, the raw data to various forms of “torturing” without having the information of the true seasonal influence only distorts the raw data. (Note that to establish empirically a hypothetical relation as a rule economist is employing known beforehand data. This is not so with respect to a seasonal influence).
We suggest that the isolation of the cyclical influence on the data is of little help as far as the understanding of the phenomenon of the business cycle is concerned. Without establishing the key causes that drive this phenomenon it is impossible to establish what type of remedies should be implemented to heal the economy.
Furthermore, if one were to accept that the data is the result of the interaction of the trend, cyclical, seasonal and irregular components, then one would conclude that these components affect the data, irrespective of human volition. Regardless of human behavior, it is the components that determine what human beings are going to do, implying a robotic behavior.
However, human action is not robotic but rather conscious and purposeful. The data is the result of people’s assessments of the facts of reality in accordance with each individual’s particular end, at a given point in time. The individual’s action is set in motion by his valuing mind and not by external factors.
The crux of the problem is that people’s responses to various seasons or holidays are never automatic but rather part of a conscious purposeful behavior. There are however, no means and ways to quantify individual’s valuations. There are no constant standards for measuring the act of a mind’s valuation of reality. On this Rothbard wrote,
It is important to realize that there is never any possibility of measuring increases or decreases in happiness or satisfaction. Not only it is impossible to measure or compare changes in the satisfaction of different people; it is not possible to measure changes in the happiness of any given person. In order for any measurement to be possible, there must be an eternally fixed and objectively given unit with which other units may be compared. There is no such objective unit in the field of human valuation. The individual must determine subjectively for himself whether he is better or worse off as a result of any change. His preference can only be expressed in terms of simple choice, or rank.3
Since it is not possible to quantify the mind’s valuation of the facts of reality, obviously this valuation cannot be put into a mathematical formulation. This in turn means that the so-called estimates of seasonal components generated by the computer programs must be of arbitrary nature.
Again, contrary to the accepted view, the adjustment for seasonality merely distorts the raw data, thereby making it much harder to ascertain the state of the business cycle. These distortions have serious implications for policy makers who employ various so-called countercyclical policies in response to the seasonally adjusted data.
The assumption by the central bank policy makers that they can quantify something that cannot be quantified is a major source of economic instability.
The business cycle is presented as something that is inherent in the economy. It is held that this mysterious something is the source of the sudden swings in economic activity.
It is however, overlooked that the swings in economic activity are the result of central bank monetary policies, which falsify interest rates, and set the platform for the generation of money out of “thin air” thereby contributing to people’s erroneous valuations of the facts of reality.
Without a coherent theory, which is based on the facts that human actions are conscious and purposeful, it is not possible to begin to understand the causes of business cycle and no amount of data torturing by means of the most advanced mathematical methods will do the trick.
Conclusion
To ascertain the state of an economy, economists are of the view that information regarding the cyclical component of economic data, such as GDP, could be of great help. Experts have concluded that to prevent a possible economic slump it is important to have the information about the magnitude of the cyclical component of the data on a short-term basis. The sooner the problem can be identified the easier it will be to fix it—so it is held. Economists are of the view that by removing the seasonal component of the data it will be possible to establish the cyclical influence. Sophisticated mathematical methods are employed to isolate the seasonal component in order to be able to ascertain the cyclical influence. We suggest that notwithstanding all the sophisticated methods that are employed, it is not possible by means of mathematical methods to establish what the boom-bust cycle phenomenon is all about. Even if it were possible to quantify the cyclical influence, without a coherent theory this would not help us to understand the causes of the business cycle.
