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Stock-Flow consistent economic models

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Etallium

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While reading through the thread about Circular Flow of Income theory I saw confusion that I think could have been prevented by better understanding of stock-flow modeling. I will put some links at the bottom of this post, but will try to summarize briefly.

Firstly, as the name implies, we need to make a clear distinction between stocks and flows.

  • Stocks describe an amount at a certain point in time. For example, how much money is in your bank account right now. Or, how much was in your bank account last Thursday at 3:35pm.
  • Flows on the other hand describe how those stocks change over a certain period of time. If you have $1,000 right now and had $700 a week ago, then your balanced increased by $300.

These two things impact each other, but are not the same thing.

The second thing we need to consider is double entry book-keeping as used in accounting. Since macro-economics describes a closed system, all flows must net to zero between parties. That is to say, for every credit there must be a debit and vice versa. By keeping track of the flows in this manner, we can better understand the corresponding changes in the stocks. Below is an example from wikipedia that shows a simple GDP model in stock-flow consistent format with 4 sectors.

stockflow-GDP.jpg

This type of models offer several advantages. From Wikipedia:
...allows for a consistent integration of the real and the financial side of the economy...
...can be used to identify unsustainable processes, for example a prolonged deficit of a sector will result in an unsustainable stock of debt...
...the consistent accounting framework prevents the modelers from leaving "black holes" i.e., unexplained parts of the model.
Click to expand...


I'd like to know your thoughts on the applicability of this form of model.

Wikipedia entry on Stock-Flow consistent models
YouTube lecture on Stock Flow models by Atoine Godin (approx. 1 hour)
 
Update: I should have made mention on the YouTube video that the stock-flow introduction is only a portion of the discussion. He also talks about many other related topics.
 
I don't know why exactly some people latched on to this epithet, especially since they presumably intend to distinguish themselves from more mainstream modeling approaches. It might not strike you immediately, but virtually everything in macroeconomic theory today is cast as a general equilibrium problem that imposes stock-flow consistency by construction. Most of those theories do not bother explaining the entire decomposition of macroeconomic aggregates, on the other hand: only a few people try to do that and it is notoriously hard to do, so maybe these people have insights we sometimes overlook.

On a personal note, I have absolutely no problem with people coming up with a different way to attack a problem. In the end, the goal is to capture key quantitative features of macroeconomic data so we can have sound discussions about policy problems. As long as we can generate precise predictions about certain correlations, certain indicators of inertia in the data, certain patterns of volatility, etc., we can have a talk about which model we should use and for what purpose. One thing I have learned from macroeconomic forecasting and machine learning is the power of combining multiple models: even simplistic models, when combined, can produce astounding forecasts. For all we know, it might be better to look at many points of view, given the limited information we have to discriminate between all those points of view.
 
TheEconomist said:
I don't know why exactly some people latched on to this epithet, especially since they presumably intend to distinguish themselves from more mainstream modeling approaches. It might not strike you immediately, but virtually everything in macroeconomic theory today is cast as a general equilibrium problem that imposes stock-flow consistency by construction. Most of those theories do not bother explaining the entire decomposition of macroeconomic aggregates, on the other hand: only a few people try to do that and it is notoriously hard to do, so maybe these people have insights we sometimes overlook.

On a personal note, I have absolutely no problem with people coming up with a different way to attack a problem. In the end, the goal is to capture key quantitative features of macroeconomic data so we can have sound discussions about policy problems. As long as we can generate precise predictions about certain correlations, certain indicators of inertia in the data, certain patterns of volatility, etc., we can have a talk about which model we should use and for what purpose. One thing I have learned from macroeconomic forecasting and machine learning is the power of combining multiple models: even simplistic models, when combined, can produce astounding forecasts. For all we know, it might be better to look at many points of view, given the limited information we have to discriminate between all those points of view.
Click to expand...

I will admit that I am coming at this from a layman's perspective. I have economic schooling, but was not trained in DSGE modeling. However, I have to consider that several prominent economists (mainstream and heterodox) criticize DSGE models for not being-stock flow consistent I'd like to understand further your statement that they are so by default. I also have to consider that Steve Keen has been developing system dynamics modeling software that maintains stock-flow consistency, which leads me to believe that the standard method is not. If you have the time to explain further, I'd like to understand your position.
 
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