By popular thinking, it is not always possible to establish the state of an economy by just inspecting the data as a whole. Instead, it is argued, what is required is to break the data down into its key components. This will enable economists to identify the true state of the economy.
Components That Drive the Data
Basically, data that is observed over time—called time series—can be broken down into four components, being: 1) the trend component; 2) the cyclical component; 3) the seasonal component; and, 4) 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 seasonal component has to do with the effect of seasons such as winter, spring, summer, autumn, and various holidays, and the irregular component displays various irregular events. It is commonly 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 also held that the isolation of this component would enable analysts to unravel the mystery of the business cycle. It is maintained that, in order to preempt the negative side effects of the business cycle, it is important to establish the size of the cyclical component on as short a duration as possible. Thus, once the central bank has identified the size of the cyclical component, it could offset the cyclical influence by means of a suitable monetary policy.
According to various studies, monthly fluctuations of the data are dominated by the influence of the seasonal component of the data. As the time span increases, the importance of the cyclical component is strengthening while the influence of the seasonal component diminishes. 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. It is also held that, while the irregular factor can be very “wild,” the effect it produces is of a short duration. Thus, the effect of a positive shock is offset by a negative shock. It then 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 impact of the seasonal component.
Removal of the Seasonal Component
Most experts consider the seasonal component of the data as known in advance. For example, every year people are likely to buy warm clothes before the arrival of the winter. Also, individuals tend to follow similar patterns of behavior year-after-year before major holidays. For instance, individuals tend to allocate a larger part of their incomes on the purchases of various consumer goods before Christmas. The assumption that the seasonal component is the same year after year implies that its removal will permit an accurate assessment of the size of the cyclical influence on the data.
By means of statistical methods, economists generate monthly estimates of the seasonal component of the data. Once this component is removed from the raw data, the data becomes seasonally adjusted. This means that we are left with the cyclical, irregular, and trend components. Since it is held that on a monthly basis, the importance of the trend component is insignificant and the effect of the irregular component is of a short duration, then fluctuations in the seasonally-adjusted data are likely to mirror the effect of the business cycle.
Most government statistical bureaus worldwide utilize US government computer programs such as X-12 and X-13 to estimate the seasonal component of the data. By means of sophisticated moving averages, these programs generate the estimates of the seasonal component. 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). It would appear that—by means of sophisticated mathematical methods—these programs can generate realistic estimates of the seasonal influence on the data, which, in turn, permits the identification of the cyclical influence. But is this the case?
If one were to accept that the data is the result of the interaction of the trend, cyclical, seasonal, and irregular components, then one could conclude that these components affect the data, irrespective of human volition. However, human action is not robotic but rather conscious and purposeful. An individual’s action is set in motion by his valuing mind, not by external factors. Data is the result of individuals’ assessments at a given point in time in accordance with each individual’s particular end. Hence, individual responses to various seasons or holidays are never automatic but rather part of a conscious, purposeful behavior. More fundamentally, there are no means available to quantify the subjective individual valuations behind his choices. There are no constant standards for measuring the act of a mind’s valuation of reality. According to Rothbard,
It is important to realize that there is never any possibility of measuring increases or decreases in happiness or satisfaction. Not only is it 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.
Since it is not possible to quantify a mind’s valuation of ends, obviously this valuation cannot be put into a mathematical formulation. Also, even though some choices can be quantified, these are not mathematical constants. This means that the so-called estimates of seasonal factors generated by the computer programs must be arbitrary numbers.
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 policymakers who employ so-called “counter-cyclical policies” in response to the seasonally-adjusted data.
Seasonally-adjusted data also form the basis of applied economics. Various theories are derived by observing the interrelationships of the seasonally-adjusted time series. These theories cannot be taken too seriously since the data behind the theory are statistically arbitrary.
Now, given that people are driven by conscious, purposeful conduct, the whole idea that a cyclical component is responsible for the boom-bust cycles is questionable. Note that the business cycle is presented by mainstream thinking as something that is just part of the economy. It is held that this “something” is the source of the sudden swings in economic activity. However, this overlooks the fact that the swings in economic activity are the result of central bank monetary policies, which falsify interest rates, and set the platform for the artificial generation of money and credit “out of thin air,” thereby contributing to people’s mistaken valuations.
Without a coherent theory—based on the fact that human actions are conscious and purposeful—it is impossible to begin to understand the causes of the business cycle and no amount of data torturing, through advanced mathematical methods, will do the trick.
Conclusion
To accurately begin to ascertain the state of an economy, many economists are of the view that information regarding the cyclical component of economic data can be of great assistance. Experts have concluded that to prevent a possible economic slump it is important to have the information about the size of the cyclical component of the data on a short-term basis. It is also held that, by removing the seasonal component, it will be possible to establish the cyclical influence.
The business cycle is presented as something that is just part of the economy. However, it is overlooked that the swings in economic activity are the result of central bank monetary policies. These policies falsify interest rates, set the platform for the artificial inflation of money and credit, contribute to economic miscalculations, and distortions of the structure of production. Even if it were possible to quantify the cyclical influence, without a coherent theory, this would be unhelpful in understanding the causes of the business cycle.
