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 reasons behind various shocks cannot be established beforehand it makes sense to look at various symptoms of the emerging economic slump. Based on these symptoms the economic doctors could decide on the medicine required either to fix the economy or to prevent it from collapsing into an economic slump.
To be able to ascertain the health of an economy, what is required is to have the necessary information i.e. the data. It is held that by analyzing the data, experts could identify the state of an economy. However, they argue that as it is not always easy to identify the health of the economy just by looking at the data — what is required is to break the data into its key components. This, it is held will enable the economist to identify the key sources of the disease.
Four Components that Drive the Data — According to Mainstream Economics
According to popular thinking the data that is observed over time —labeled as time series — is determined by four components, these are:
1. The trend component
2. The cyclical component
3. The seasonal component
4. The irregular component
It is accepted that the trend determines the general direction of the data over time, while the cyclical component causes movements that are related to the business cycle. The influence of seasons like winter, spring, summer, and autumn and various holidays is conveyed by the seasonal component. The irregular component depicts the various irregular events. It is held that the interplay of these four components generates the final 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 the analysts to unravel the mystery of the business cycle. Moreover, to pre-empt the negative effect of the business cycle on people's wellbeing it is important to observe the magnitude of the cyclical component on as short a duration basis as possible. Like any disease, 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 its 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 factor. As the time span increases, the importance of the cyclical factor rises while the influence of the seasonal factor 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 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. Thus, the effect of positive shocks is offset by negative shocks.
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 — Seasonal Adjustment
Most economists consider the seasonal component of the data as constant and hence known in advance. For example, every year people buy warm clothes before the arrival of the winter not before the arrival of the summer.
In addition, people follow a similar pattern of behavior year-after year before major holidays. Also, 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 component of 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.
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 imply that these components are inserted into the data irrespective of human volition. Regardless of human behavior it is these 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 reality in accordance with each individual's particular end, at a given point in time.
An individual's action is set in motion by his valuing mind and not by external factors. This in turn means that individuals are not expected to follow the exact pattern of behavior year after year. Changes in individual’s goals will produce different responses towards holidays or seasons of the year.
Currently most government statistical bureaus worldwide utilize the US government computer programs X-11, X-12 and X-13 Arima Seats 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 de-seasonalize the data (i.e. adjust for seasonality). 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 seasonal components 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 components of the data, which in turn will permit to ascertain the cyclical component.
Note again that economic experts are interested to establish the state of the cyclical component of the data, such as the Gross domestic product or employment in order to form a judgment regarding the state of the so-called economy.
The strength of the seasonal components and in turn the cyclical components could determine the direction of the central bank policy i.e. whether the central bank will tighten or loosen its interest rate stance.
The extraction of the cyclical component of the data, however, is of little help as far as understanding of the phenomenon of the business cycle is concerned. Without understanding the key causes that drive this phenomenon it is impossible to establish what remedies should be implemented to heal the economy. Without a coherent theory, which is based on identifying the primary key causes of boom-bust cycles, no amount of data torturing by means of the most advanced mathematical methods will do the trick. Consequently, the central bank tampering with the economy in response to seasonally adjusted data rather than mitigating the boom-bust cycles only strengthens this menace.
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 isolate the cyclical component. However, notwithstanding all the sophisticated methods that are utilized, without the employment of the “cause and effect” principle it is not possible by means of statistical methods to ascertain what the boom-bust cycle phenomenon is all about. Consequently, it is not possible to establish what suitable methods should be implemented to counter the boom-bust cycle phenomenon. Various central bank policies that are acting upon information obtained from seasonally adjusted data only generating more economic instability. If mainstream economists were to implement the cause and effect framework they would discover that the key cause of boom-bust cycles is the policies of the central bank. Note that to reach this conclusion we do not require sophisticated seasonally adjusted methods but the implementation of the cause and effect framework that views individuals as humans and not as machines.
