link to the first part of a two-part primer), I just want to make some brief remarks about how it ties into the notion of forecastability (description). Should we be able to expect to forecast the business cycle?
(This article is fairly brief, and re-iterates points I have made in other articles. Travelling and major home renovations have cut into my writing time recently.)
In order to forecast profits, we need to forecast investment. And why do private firms invest? Because they expect future profits. Can we predict what private firms will do?
(One can presumably forecast various economic variables based on various relationships that hold in steady state. For example, we could probably pin down many of the components of the CPI index on a forecasting horizon of a few months. The problem is that longer term CPI forecasts will end up being conditional on avoiding a recession. I am not particularly interested in those short-term conditional forecasts, but the more intractable business cycle forecasting problem.)
A mathematical model that is based on measured economic variables builds in an assumption that firms will react mechanically to the variables in the model. One could spend days developing intricate mathematical models of recession probabilities based on the 2-/10-year slope, but we need to ask ourselves: why should any firm care about that slope? The reason why the 20-/10-year slope historically had predictive powers was because fixed income analysts priced those instruments based on their views about the cycle. There is no reason that those fixed income analysts will always be correct in their assessments. Furthermore, if you are a fixed income analyst (part of my target audience) you should not be using the 2-/10-year slope as an input into your model that determines the fair value of the 2-/10-year slope!
The 1990s tech cycle provides a good example of the limitations of such mathematical models. Firms were engaged in massive infrastructure investment to meet projected demand for products that did not even exist from a customer perspective. The 3G wireless and bandwidth capacity was eventually used -- but the take-up was too slow to save a lot of the firms behind the investment. Models calibrated against historical data could not capture this, as current capacity utilisation and profitability did not matter -- only the projections. Meanwhile, since these were new markets, there was nothing to calibrate those projections against.
One could try to model this by creating a variable that is not directly measured, but is instead inferred from historical data (like the output gap). This variable could be labelled "animal spirits." The model will then assume that firms mechanically react to "animal spirits."
This will work during an expansion -- investment does seem to follow trends. However, we have no way of predicting the direction of animal spirits. We could find out that our estimate for animal spirits falls in response to recession, but back-casting a recession is not exactly the most impressive analytical feat.
In summary, business cycle analysis mainly revolves around the determination of fixed investment (although we will need to keep an eye on other potential pitfalls, such as policy errors).
(c) Brian Romanchuk 2018
Wednesday, July 11, 2018
The Kalecki Profit Equation And Forecasting
Note: Posts are manually moderated, with a varying delay. Some disappear.
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RE: Brian, Romanchuk, The Kalecki Profit Equation And Forecasting
The Kalecki Profit Equation is provably false. For details see
Truth by definition? The Profit Theory is axiomatically false for 200+ years
Don’t have time to look at this now; I guess I will have to be provably incorrect.Delete
I just noticed that some of your other comments got munched by the spam filter; I restored them.
"predicting the direction of animal spirits"ReplyDelete
The Kalecki Profit Equation presupposes several conditions that allow the result you described. Animal spirits are not included.
What if we had a model that allowed animal spirits? Here is such a model:
We have two sectors-business and households. These sectors are unlike the Kalecki sectors. Here the business sector is like a machine not-yet-placed-into motion. This business machine is capable of production once populated with human direction. Until populated with humans and motivated, it is an inert object. It is owned by decision makers at every scale.
[Think of a volunteer fireman district as an example. The fire truck resides in the fire station until activated with volunteers leaving their normal daily routine. When the district is called into action and activated with people, fire suppression becomes a viable product.]
The household sector is completely incapable of production but can consume production. All of the owners and human motivators of every scale are part of the household sector.
There is an obvious gap between these two sectors. Business requires motivation by humans; households require production by business before consumption can occur. How do we bridge the gap?
Trying to keep the model relevant, we will allow the household sector to produce 'money'. We can argue what money 'is' but in modern capitalistic societies, money is produced by government, with government controlled by people. Money is assumed (in this model) to motivate people including owners.
Now we can add 'animal spirits' to the model. We could also add more mundane things like the regular need for food.
We may want to begin the model in midstream with a functioning economy. The Kalecki Profit formulation may not work well unless we presuppose the location of the motivating money supply as being in the ownership of business owners. At the other extreme, households may widely own money and lack motivation to use it for any purpose.
NMW, this model requires animal spirits to start the simplest movement. Routine activity like food production requires daily motivation but this certainly present. Cyclical investment would require a cyclical motivation which might be found in the money supply linkage.
Roger, we want models that line up with observed behaviour. The Kalecki profit equation corresponds to the sectoral decomposition used in the national accounts, albeit with simplifications. Trying to invent new decompositions of the aggregates has the problem that we no longer have any data to calibrate against.Delete
Furthermore, you seem to be arguing that business decisions are made by owners. This does not meet the reality of managerial capitalism. I own equities, and I am not receiving a lot of call from CEOs asking for my opinion of their business plans.
The national accounts are severely distorted by adjustments for investment making them, in my judgement, unsuited for accurate modeling. That said, they are the best data we have.ReplyDelete
I would grant that decisions for large business firms are indeed made by managers. My suggested models are more suited for smaller business entities employing people on a seasonal or task specific product. There are a lot of these entities and they are seldom part of organized labor.
That said, there are many large organizations that make things on a contractual basis such as aircraft which wax and wane depending largely upon political decision. My prediction would be that the Kalecki profit equation would fail completely on these entities.
Roger, the Kalecki Profit Equation is not predictive, it is an accounting identity. If we take the standard definition of profits (which Egmont argues is "provably incorrect," but we will put that aside for now), it is basic algebra to show that the terms have to add up that way.Delete
If your model adds up profits differently, your model is not using the standard definitions.