By Dr Frank Shostak
By popular thinking, 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 economist to identify the true state of the economy.
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. 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 maintained that in order to pre-empt the negative side effects of the business cycle on individuals’ wellbeing, it is important to establish the magnitude of the cyclical component on as a short duration basis as possible. 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. As the time span increases, the importance of the cyclical component increases 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.
Whilst 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.
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. In addition, individuals follow similar patterns of behavior year-after-year before major holidays. Thus, individuals tend to spend a larger part of their incomes before Christmas.
The assumption that the seasonal component is the same year after year means that its removal 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 component of a data. Once this component is removed from the raw data, the data becomes seasonally adjusted.
Note that we are left with the cyclical, the irregular and the trend components. 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 component of a data. By means of sophisticated moving averages, these programs generate 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. 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.
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.
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 economic theory to back it up. If the data appears to be very choppy then a high degree of a moving average is applied. Conversely, a lower moving average is employed for a lesser volatile data.
In the process of calculating the seasonal component, the computer program produces estimates for the trend and cycle component using either a weighted 9-term moving average or, a weighted 13-term, or a weighted 23-term moving average.
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.
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 behaviour. 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.
This in turn means that the so-called estimates of a seasonal component generated by the computer programs must be of arbitrary nature.
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 counter-cyclical 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.
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. 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.