Cycles in the Economy

Cycles is a controversial issue in economics. Economists do not agree on what causes cycles, and in some instances they cannot even agree on whether they exist at all. Especially, in financial markets, the existence of cycles is widely debated.

Cycles in a system are generally caused by delayed response effects. A delayed response system is a system where the future development of the system is dependent on the state of the system in the more or less distant past. As an example, the population of a country is a delayed response system. A period of high birth rates will give rise to another period of high birth rates some twenty to thirty years later. Another example of a delayed response system is the mining industry, where increased exploration tends to increase production at some point several years into the future.

A delayed response system is, however, not sufficient to cause cycles. Typically, at lease two interacting delayed response systems are needed to cause a cycle. In the case of population in an eco system, for example, the balance between predator and prey is an example of two interacting delay response systems. As long as prey is abundant, the number of predators will grow exponentially. Similarly, if the number of predators is small, the prey will also be able to increase in number. As the number of predators grow, they will take their toll on the newborn prey. The number of prey will start to decrease and eventually, the predators will starve, and the weakest will die.

In the economy, a similar example is the investment cycle. When business in a specific sector is profitable, there is an incentive for companies to invest in increasing investment capacity. After a time lag, the increased capacity comes online. It is common for capacity to overshoot demand. This causes consolidation and lowered prices in the industry. These lower prices will stimulate demand, while at the same time lowering investments. Eventually, low investment will reduce production capacity. This causes prices to rise and make the business profitable again. But as prices rise, there is also an incentive for consumers to reduce demand and try to find replacement products. The cycle starts all over again.

There is a difference in cycles between linear and non-linear systems. In a linear system, cycles are exactly repeating with a fixed length. If there are non-linear effects, however, cycles become unpredictable and typically don't ever repeat exactly. This is also known as chaotic behavior. The economy is an example of a chaotic system. There is no point in trying to make forecasts of the economy far into the future, because the length of the different economical cycles vary, and there is no way to know where in the cycle you will be, say fifty years from now. This is also the reason why cycles are so hard to detect using historical time series data. For linear cycles, there are powerful mathematical methods, such as Fourier analysis, that can be used to determine the duration of cycles with great accuracy.

Examples of delayed response mechanisms
Cause, change in:	Effect, change in:	Delay
Interest rate changes	Capital spending	6-18 months
Birth rate	Birth rate	20-30 years
Business profitability	Production capacity	1-2 years
Natural resources prospecting	Production	3-10 years
Stock exchange prices	Real estate prices	6-12 months
Employment	Consumer spending	3-6 months
Price	Demand	0-24 months
Salaries of a category of workers	Availability of such workers	3-8 years
Credit availability	Number of bad loans	1-5 years
Money stock	Inflation	6-18 months

Non-linear cycles are incredibly difficult to find and to use for forecasting. There are no mathematical general purpose cookbook methods to find non-linear cycles. For that reason, there are few models that utilize the cyclical nature of the economy. We believe that there is only one effective way to make such models - to identify the delayed response mechanisms involved. There simply are no short-cuts.

When making a model of the economy, it is important to focus on the delayed response effects. Trying to model the non-linear cycles directly is doomed to failure. And ignoring the cycles will make the model very unreliable at cycle turning points, even for the short term. If the delayed response effects are modeled correctly, however, the cyclical behavior will follow automatically.