Making sense of macroeconomics

The reputation of macroeconomics took a battering in the wake of the global financial crisis after failing to predict the great recession. Although much of the criticism by outsiders is misplaced, there are some grains of truth and many academic economists would agree that there are many areas where economics needs to improve.

This collection of papers from the Oxford Review of Economic Policy looks at the state of macroeconomics today and provides a range of opinions from leading macroeconomists. More importantly, it shines the spotlight on those areas where economics can be seen to have failed and offers some suggestions about how to take us forward (the papers are not particularly technical and as such are relatively accessible. Credit should also go to the publishers, Oxford University Press, for taking this volume from out behind the paywall).

David Vines and Samuel Wills make the point that macroeconomics has been here before – in the early 1930s and again in the 1970s, and both times the discipline evolved to try and make sense of changed circumstances. But in order to identify what has to change, we need to know where we are and what is wrong. At the centre of the debate stand New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models, which form the workhorse model for policy analysis. 

The general consensus is that they are not fit for purpose – a point I have made before (here and here). Such models are based on microfounded representative-agents – a theoretical approach which postulates that there is a typical household or firm whose behaviour is representative of the economy as a whole. I have always rather struggled with this approach because it assumes that all agents respond in the same way – something we know is not true in the case of households given differing time preferences, depending on age and educational attainment. An additional assumption that underpins such models is that expectations are formed rationally – something we know is not always true.

Thus the consensus appears to be that these two assumptions need to be relaxed if macroeconomics is going to be more relevant for future policy work. You might say that it is about time. Indeed it is a sad indictment that it took the failure of DSGE models during the financial crisis to convince proponents that their models were flawed when it was so obvious to many people all along.

In order to understand this failure, Simon Wren-Lewis offers an explanation as to why this form of thinking became so predominant to the exclusion of other types of model. He argues that the adoption of rational expectations was “a natural extension of the idea of rationality that was ubiquitous in microeconomic theory” and that “a new generation of economists found the idea of microfounding macroeconomics very attractive. As macroeconomists, they would prefer not to be seen by their microeconomic colleagues as pursuing a discipline with seemingly little connection to their own … Whatever the precise reasons, microfounded macroeconomic models became the norm in the better academic journals.” Indeed, Wren-Lewis has long argued that since academics could only get their work published in top journals if they went down this route, this promoted an “academic capture” process which led to the propagation of a flawed methodology.

Wren-Lewis also makes the point that much of so-called cutting edge analysis is no longer constrained to be as consistent with the data as was once the case. He notes that in the 1970s, when he began working on macro models “consistency with the data was the key criteria for equations to be admissible as part of a model. If the model didn’t match past data, we had no business using it to give policy advice.” There is, of course, a well-recognised trade-off between data coherency and theoretical consistency, and I have always believed that the trick is to find the optimal point between the two in the form of a structural economic model. It does not mean that the models I use are particularly great – they certainly would not make it into the academic journals – but they do allow me to provide a simplified theoretical justification for the structure of the model, in the knowledge that it is reasonably consistent with the data.

Ultimately one of the questions macroeconomists have to answer more clearly – particularly to outsiders – is what are we trying to achieve? Although much of the external criticism zooms in on the failure of economists to forecast the future, what we are really trying to do is better understand how the economy currently works and how it might be expected to respond to shocks (such as the financial crisis). Olivier Blanchard  believes that “we need different types of macroeconomic models for different purposes” which allows a continued role for structural models, particularly for forecasting purposes. Whilst I agree with this, I have still not shaken off the conviction, best expressed by Ray Fair back in 1994 (here p28), that the structural model approach “is the best way of trying to learn how the macroeconomy works.” Structural models are far from perfect, but in my experience they are the least worst option at our disposal.