Various statistics that governments produce on a regular basis carry the label “seasonally adjusted”. What is the meaning of this label? According to popular thinking the data that is observed over time (labelled as time series) is determined by four factors, which are:
- The trend factor
- The cyclical factor
- The seasonal factor
- The irregular factor
It is accepted that the trend determines the general direction of the data over time, while the cyclical factor 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 factor. The irregular factor depicts the effect of various irregular events. It is held that the interplay of these four factors generates the final data.
Popular thinking regards the cyclical influence as the most important part of the data. It is held that the isolation of this influence 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 well being it is important to observe the influence of the cyclical factor 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. Once the central bank has identified the size of the cyclical influence it could offset this influence by means of a suitable monetary policy, so it is held.
According to various statistical studies the monthly fluctuations of data are dominated by the influence of the seasonal factor.1 As the time span increases, the importance of the cyclical influence rises while the influence of the seasonal factor diminishes. The cyclical influence will be more powerful in quarterly data than in monthly data. The trend, it is assumed, exerts a strong influence on a yearly basis while having an insignificant effect on the monthly variations of the data. While the irregular factor can be very “wild”, however, it is held the effect it produces is of a short duration. With the effect of positive shocks are 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 influence is not affected in the process.
Most economists regard the seasonal effect 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 behaviour year-after year before major holidays. For example, people tend to spend a larger fraction of their incomes before Christmas. The assumption that the seasonal influence is constant year after year means that its removal will not distort the influence of the cyclical factor. This in turn will permit an accurate assessment of the business cycle effect. By means of statistical methods, economists generate numbers for each month that supposedly provide an estimate of the seasonal effect. The data becomes seasonally adjusted once these computed numbers are subtracted from the raw data.
Despite the application of various statistical and mathematical methods to extract the seasonal influence, which varies in degree of complexity, the entire framework is based on arbitrary assumptions that have nothing to do with reality. If one were to accept that the data is the result of the interaction of the trend, cyclical, seasonal and irregular factors, then one would conclude that only these factors affect the data, irrespective of human volition. Regardless of human behaviour it is these factors that determine what human beings are going to do, implying a robotic behaviour.
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. The individual’s action is set in motion by his valuing mind and not by external factors. This in turn means that the constancy of various seasons does not mean that individuals are expected to follow the exact pattern of behaviour year after year. Changes in individual goals will produce different responses towards holidays or seasons of the year. Consequently a framework, which disregards that humans are not robots and treats the seasonal effect as being constant, will contribute to the wrong assessment regarding the state of the cyclical influence.
Currently most government statistical bureaus worldwide utilise the US government computer programs X-11, X-12 and X-13 to estimate the seasonal influence on data. By means of sophisticated moving averages, these programs generate estimates of the seasonal effect. The computer program then uses the obtained estimates to de-seasonalise the data i.e. adjust for seasonality. Designers of these seasonal adjustment computer programs have attempted to address the issue of the constancy of the seasonal effect by allowing this effect to vary over time. For example, the seasonal effect for retail sales in December will not be the same year after year but will rather vary. Furthermore, these programs instructed to establish whether the seasonal effect is stable. 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. The truth is that these programs generate arbitrary figures, which have nothing to do with the reality.
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 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.21
Since it is not possible to quantify a 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 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 policy makers who employ various so-called counter-cyclical policies in response to the seasonally adjusted data. For example, the strength of the seasonally adjusted employment data could determine whether the central bank would raise or lower interest rates. This pretense by the central bank policy makers that they can quantify something that cannot be quantified is a major source of economic instability.
The seasonally adjusted data also forms the basis of so-called applied economics. Various theories are derived by observing the inter relationships of the seasonally adjusted time-series.
The whole idea that by being able to observe the influence of the business cycle could enhance our understanding of this phenomenon is fallacious. The business cycle here 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.
Even if it were possible to quantify the cyclical influence, this would not help us to understand what the business cycle is all about. Without a coherent theory, which is based on the fact that human actions are conscious and purposeful, it is not possible to begin to understand the causes of business cycles and no amount of data torturing by means of the most advanced mathematical methods will do the trick.
