Econometric forecasting and mathematical ‘rigour’21 December, 2016 at 20:32 | Posted in Statistics & Econometrics | 3 Comments
There have been over four decades of econometric research on business cycles … The formalization has undeniably improved the scientific strength of business cycle measures …
But the significance of the formalization becomes more difficult to identify when it is assessed from the applied perspective, especially when the success rate in ex-ante forecasts of recessions is used as a key criterion. The fact that the onset of the 2008 financial-crisis-triggered recession was predicted by only a few ‘Wise Owls’ … while missed by regular forecasters armed with various models serves us as the latest warning that the efficiency of the formalization might be far from optimal. Remarkably, not only has the performance of time-series data-driven econometric models been off the track this time, so has that of the whole bunch of theory-rich macro dynamic models developed in the wake of the rational expectations movement, which derived its fame mainly from exploiting the forecast failures of the macro-econometric models of the mid-1970s recession …
These observations indicate … that econometric methods are limited by their statistical approach in analysing and forecasting business cycles, and more over, that the explanatory power of generalised and established theoretical relationships is highly limited when applied to particular economies during particular periods alone, that is, if none of the local and institution-specific factors are taken into serious consideration …
The wide conviction of the superiority of the methods of the science has converted the econometric community largely to a group of fundamentalist guards of mathematical rigour … So much so that the relevance of the research to business cycles is reduced to empirical illustrations. To that extent, probabilistic formalisation has trapped econometric business cycle research in the pursuit of means at the expense of ends.
The limits of econometric forecasting has, as noted by Qin, been critically pointed out many times before. Trygve Haavelmo — with the completion (in 1958) of the twenty-fifth volume of Econometrica — assessed the the role of econometrics in the advancement of economics, and although mainly positive of the “repair work” and “clearing-up work” done, Haavelmo also found some grounds for despair:
There is the possibility that the more stringent methods we have been striving to develop have actually opened our eyes to recognize a plain fact: viz., that the “laws” of economics are not very accurate in the sense of a close fit, and that we have been living in a dream-world of large but somewhat superficial or spurious correlations.
And Ragnar Frisch also shared some of Haavelmo’s doubts on the applicability of econometrics:
I have personally always been skeptical of the possibility of making macroeconomic predictions about the development that will follow on the basis of given initial conditions … I have believed that the analytical work will give higher yields – now and in the near future – if they become applied in macroeconomic decision models where the line of thought is the following: “If this or that policy is made, and these conditions are met in the period under consideration, probably a tendency to go in this or that direction is created”.
Maintaining that economics is a science in the ‘true knowledge’ business, I remain a skeptic of the pretences and aspirations of econometrics. The marginal return on its ever higher technical sophistication in no way makes up for the lack of serious under-labouring of its deeper philosophical and methodological foundations that already Keynes complained about. The rather one-sided emphasis of usefulness and its concomitant instrumentalist justification cannot hide that the legions of probabilistic econometricians who give supportive evidence for their considering it ‘fruitful to believe’ in the possibility of treating unique economic data as the observable results of random drawings from an imaginary sampling of an imaginary population, are scating on thin ice.
A rigorous application of econometric methods in economics really presupposes that the phenomena of our real world economies are ruled by stable causal relations between variables. The endemic lack of predictive success of the econometric project indicates that this hope of finding fixed parameters is a hope for which there, really, is no other ground than hope itself.