The ongoing online debate regarding mainstream Dynamic Stochastic General Equilibrium (DSGE) models has again heated up. Professor Simon Wren-Lewis of Oxford discussed whether mainstream macro was eclectic; provoking a response from Professor Lars P. Syll of Malmö University, the latest of which is "'Deep Parameters' And Microfoundations". I am not interested in the entire spectrum of their debate, rather on the question of eclectic models. (This is the first part of a two-part series.)
Axiomatic Nature Of DSGE MacroIn an earlier article, Professor Syll complains about the "axiomatic" nature of mainstream macro; although I am not a fan of DSGE macro, I do not see this as being a problem. The mathematical affectation of DSGE macro is somewhat reasonable given the academic context the theorists work in.
Modern formal mathematics consists of moving from an initial set of assumptions about a mathematical "system," and then seeing what new properties of that system can be derived using the rules of mathematical logic. Therefore, it is not unexpected that papers would be written with a formalism of starting from a set of assumptions, and then moving towards conclusions. You can skip over specifying your starting assumptions -- which is commonly done in math courses for non-mathematicians -- but this is sloppy, and can lead to errors in comprehension.
Instead, the only problem that I see is that much of the mathematics within DSGE macro is there to make the paper "mathy," and not to inform. Unexplained leaps of logic are unfortunately common.
I would note that not all aspects of economics can be treated with mathematical models. For example, economic history should follow the academic standards for historical analysis. However, I am mainly interested in questions that are quantitative in nature, and so my discussion here is only focussed on those parts of the subject.
Types Of Mathematical Economic ModelsReturning to Professor Wren-Lewis' discussion of eclectic models, I should note that I roughly in agreement with him. I also think that we need to work with a variety of simple models, none of which are full simulation of an economy. (This is in distinction to the large models that reached their peak of popularity during the 1960s/1970s.) That said, we distinctly disagree about what set of economic models should look at; he generally favours DSGE models, while I think that entire modelling approach should be abandoned. I will turn to the specific problems of DSGE models in the second part of this article.
I see three potential modelling strategies in macro.
- Big Models. A large model that attempts to describe the entire economy, with specific sectors modelled (and presumably sectors in foreign economies as well).
- Aggregated Models. Smaller models that attempt to capture the broad trends within the entire economy; the business sector is either treated as a single sector, or possibly a small number of sectors (e.g., consumer goods and capital goods).
- Partial Models. Models that attempt to capture dynamics of a small aspect of the economy. Other parts of the economy would not be specified; analysis would be conditional on a forecast for broad economic trends. (For example, we could model the housing market, and have the unemployment rate, wage growth, and interest rates as external ("exogenous") variables.) If you use this model to generate forecasts, those forecasts are conditional upon your forecast for those input variables.
The first strategy (big models) would be very attractive -- if it worked. These models were the focus of research in the 1960s, but they have largely been abandoned. I have not directly worked with such models myself, but I have heard enough horror stories from people who had to work on the plumbing of those models to say that they did not work in practice. I will give a slightly more formal reason for my rejection below.
The second strategy (aggregated models) might be similar to the first type, but the model is working with aggregated behaviour. This makes it harder to align the model data to measured economic data; for example, do we use CPI or PCE to stand in for the price index within a model? The DSGE models at central banks are generally aggregated in this fashion. My preferred alternative is the use of Stock-Flow Consistent (SFC) models (my page on SFC models).
The use of this second class of models is why I doubt the viability of the first class. If we have a model that properly incorporates business sector planning and investment, the model will end up embedding the Kalecki profit equation. What that relationship tells us is that increasing dividends and investment raises profits. Since the objective of investment is to increase profits, the model should end up with a "positive feedback loop"; pro-cyclical investment drives the business cycle. Profit growth continues until something impairs fixed investment, at which point the cycle goes into reverse.
The propensity of businesses to invest will drive activity within the model (unless government activity is even more volatile); we could call this tendency "animal spirits." Forecasting the path of the model economy entirely turns into forecasting animal spirits. I will assert that forecasting animal spirits is difficult; for a proof, examine business stories at the time of writing (January 2015). (Standard DSGE models avoid the problem of "animal spirits" by almost completely ignoring business investment decisions. This has the side effect of business cycles disappearing from the models; the models can only induce recessions via dubious concepts like "productivity shocks.")
The hard to measure concept of animal spirits is what dooms the big macro models. The uncertainty around animal spirits dwarfs any additional information that we can glean from dis-aggregating sectors.
The final strategy, partial models, are what are mainly looked at in financial market research. These models are easier to estimate, but hugely depend upon the quality of the inputs you supply for the broad macro trends. Moreover, they are unsatisfying to academia, since they can be incoherent -- a set of models for various sectors may generate inconsistent forecasts if you aggregate them.
Eclectic By Necessity
We end up with a need for eclectic models by necessity. If "big models" worked well, they would be all that you need. Unfortunately, they do not, so we are stuck with partial models. They are either aggregated models which are possibly too abstract to fit to real world data (or entirely depend upon non-measurable variables), or partial models that rely on forecasts for broad economic trends. Although the details of my argument may differ from that of Professor Wren-Lewis, we end up with a similar view on the need for many models.
Where we differ is on the acceptability of DSGE models within that mix of models. I will turn to this in the second part.
(c) Brian Romanchuk 2015