The National Bureau of Economic Research (NBER) has a standing committee which designates the start and finish of recessions, or more precisely, the dates of the peaks and troughs of the US business cycle.
And the NBER site maintains a complete record of the US business cycle, dating back to the middle 1800’s, as shown in the following tables.
Periods of contraction, from peak to trough, are typically shorter than periods of expansion – or the movement from previous trough to the next peak.
Since World War II, the average length of the business cycle, variously measured from trough to trough or from peak to peak, is more than 5 years.
Focusing on the current situation, we are interested in the length of time from the previous peak of the business cycle in December 2007 to the next peak. The longest peak to peak period was over the prosperity of the 1990’s, and lasted more than 10 years (128 months).
So, it would be unusual if the peak of this current business cycle were much later than 2017-2018.
In terms of predicting turning points, matters are complicated by the fact that, unlike many European countries, the NBER does not define a recession in terms of two consecutive quarters of decline in real GDP.
Rather, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.
But just predicting the onset of two consecutive quarters of decline in real GDP is challenging. Indeed, the record of macroeconomic forecasting is very poor in this regard.
Part of the problem with the concept of a “cycle” in this context is the irregularity of the fluctuations derived by standard filters and methods.
Harvey, for example, applies low band and pass Butterworth filters to US total investment and other macroeconomic series, deriving, at one pont, an investment “cycle” that looks like this.
So almost everything that makes a cycle useful in prediction is missing from this investment cycle. Thus, one cannot conclude that a turning point will occur, when the amplitude of the cycle is reached, since the amplitudes of these quasi-cycles vary considerably. Similarly, the “period” of the cycle is by no means fixed, but is basically stochastic, with a certain variance sometimes expressed as a “hyperparameter.” Only a certain quality of smoothness presents itself, and, of course, is a result of the filtering parameters that are applied.
In my opinion, industry cycles make a certain amount of sense, for particular industries, over particular spans of time. What I mean is that identification of such industry cycles improves predictability of the underlying series – be it sales or inventories or what have you.
The business cycle, on the other hand, is something of a metaphor, or maybe just an evocative phrase.
True, there are periods of economic contraction and periods of expansion.
But the extraction of macroeconomic cycles often does not improve predictability, because the fluctuations so identified are highly irregular from a number of different viewpoints.
I’ve sort of confirmed this is a quantitative sense by applying various cycle-extraction softwares to US real GDP to see whether any product or approach gave a hint that the Great Recession which began in 2008 would (a) occur, and (b) be as dramatic as it was. So far, no go.
And, of course, Ng points out that the Great Recession was fundamentally different than, say, recessions in the 1960’s sand 1970’s in that it was a balance sheet recession.