Wednesday 24 April 2019

A retrospective on macro modelling

Anyone interested in the recent history of economics, and how it has developed over the years, could do worse than take a look at the work of Beatrice Cherrier (here). One of the papers I particularly enjoyed was a review of how the Fed-MIT-Penn (FMP) model came into being over the period 1964-74, in which she and her co-author, Roger Backhouse, explained the process of constructing one of the first large scale macro models. It is fascinating to realise that whilst macroeconomic modelling is a relatively easy task these days, thanks to the revolution in computing, many of the solutions to the problems raised 50-odd years ago were truly revolutionary.

I must admit to a certain nostalgia when reading through the paper because I started my career working in the field of macro modelling and forecasting, and some of the people who broke new ground in the 1960s were still around when I was starting out in the 1980s. Moreover, the kinds of models we used were direct descendants of the Fed-MIT-Penn model. Although they have fallen out of favour in academic circles, structural models of this type are in my view still the best way of assessing whether the way we think the economy should operate is congruent with the data. They provide a richness of detail that is often lacking in the models used for policy forecasting today and in the words of Backhouse and Cherrier, such models were the “big science” projects of their day.

Robert Lucas and Thomas Sargent, both of whom went on to win the Nobel Prize for economics, began in the 1970s to chip away at the intellectual reputation of structural models based on Keynesian national income accounting identities for their “failure to derive behavioral relationships from any consistently posed dynamic optimization problems.” Such models, it was argued, contained no meaningful forward-looking expectations formation processes (true) which accounted for their dismal failure to forecast the economic events of the 1970s and 1980s. In short, structural macro models were a messy compromise between theory and data and the theoretical underpinnings of such models were insufficiently rigorous to be considered useful representations of how the economy worked.

Whilst there is some truth in this criticism, Backhouse and Cherrier remind us that prior to the 1970s “there was no linear relationship running from economic theory to empirical models of specific economies: theory and application developed together.” Keynesian economics was the dominant paradigm, and such theory as there was appeared to be an attempt to build yet more rigour around Keynes’ work of the 1930s rather than take us in any new direction. Moreover, given the complexity of the economy and the fairly rudimentary data available at the time, the models could only ever be simplified versions of reality.

Another of Lucas’s big criticisms of structural models was the application of judgement to override the model’s output via the use of constant adjustments (or add factors). Whilst I accept that overwriting the model output offends the purists, it presupposes that economic models will outperform relative to human judgement. But such an economic model has not yet been constructed. Moreover, the use of add factors reflects a certain way of thinking about modelling the data. If we think of a model as representing a simplified version of reality, it will never capture all the variability inherent in the data (I will concede this point when we can estimate equations, all of which have an R-bar squared close to unity). Therefore, the best we can hope for is that the error averages zero over history – it will never be zero at all times. 

Imagine that we are in a situation where the last historical period in our dataset shows a residual for a particular equation which is a long way from zero. This raises a question of whether the projected residual in the first period of our forecast should be zero. There is, of course, no correct answer to the question. It all boils down to the methodology employed by the forecaster – their judgement – and the trick to using add factors is to project them out into the future so that they minimise the distortions to the model-generated forecast.

But to quote Backhouse and Cherrier, “the practice Lucas condemned so harshly, became a major reason why businessmen and other clients would pay to access the forecasts provided by the FMP and other macroeconometric models … the hundreds of fudge factors added to large- scale models were precisely what clients were paying for when buying forecasts from these companies.” And just to rub it in, the economist Ray Fair ”later noted that analyses of the Wharton and Office of Business Economics (OBE) models showed that ex-ante forecasts from model builders (with fudge or add factors) were more accurate than the ex-post forecasts of the models (with actual data).

Looking back, many of the criticisms made by Lucas at al. seem unfair. Nonetheless, they had a huge impact on the way in which academic economists thought about the structure of the economy and how they went about modelling it. Many academic economists today complain about the tyranny of microfoundations, in which it is virtually impossible to get a paper published in a leading journal without linking models of the economy to them. In addition, the rational expectations hypothesis has come to dominate in the field of macro modelling, despite the fact there is little evidence suggesting this is how expectations are in fact formed.

As macro modelling has developed over the years, it has raised more questions than answers. One of the more pervasive is that, like the models they superseded, modern DSGE models have struggled to explain bubbles and crashes. In addition, their treatment of inflation leaves a lot to be desired (the degree of price stickiness assumed in new Keynesian models is not evident in the real world). Moreover, many of the approaches to modelling adopted in recent years do not allow for a sufficiently flexible trade-off between data consistency and theoretical adequacy. Whilst recognising that there are considerable limitations associated with structural models using the approach pioneered by the FMP, I continue to endorse the view of Ray Fair who wrote in 1994 that the use of structural models represents "the best way of trying to learn how the macroeconomy works."

No comments:

Post a Comment