by Calibration — how to perform decades of scientific fraud One may wonder how much calibration adds to the knowledge of economic structures and the deep parameters involved … Micro estimates are imputed in general equilibrium models which are confronted with new data, not used for the construction of the imputed parameters … However this procedure to impute parameter values into calibrated models has serious weaknesses … First, few ‘deep parameters’ have been established at all … Second, even where estimates are available from micro-econometric investigations, they cannot be automatically importyed into aggregated general equlibrium models … Third, calibration hardly contributes to growth of knowledge about ‘deep parameters’. These deep parameters are confronted with a novel context (aggregate time-series), but this is not used for inference on their behalf. Rather, the new context is used to fit the model to presumed ‘laws of motion’ of the economy … This leads to the fourth weakness. The combination of different pieces of evidence is laudable, but it can be done with statistical methods as well … This statistical approach has the advantage that it takes the parameter uncertainty into account: even if uncontroversial ‘deep parameters’ were available, they would have standard errors. Specification uncertainty makes things even worse. Negecting this leads to self-deception.
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Calibration — how to perform decades of scientific fraud
One may wonder how much calibration adds to the knowledge of economic structures and the deep parameters involved … Micro estimates are imputed in general equilibrium models which are confronted with new data, not used for the construction of the imputed parameters … However this procedure to impute parameter values into calibrated models has serious weaknesses …
First, few ‘deep parameters’ have been established at all …
Second, even where estimates are available from micro-econometric investigations, they cannot be automatically importyed into aggregated general equlibrium models …
Third, calibration hardly contributes to growth of knowledge about ‘deep parameters’. These deep parameters are confronted with a novel context (aggregate time-series), but this is not used for inference on their behalf. Rather, the new context is used to fit the model to presumed ‘laws of motion’ of the economy …
This leads to the fourth weakness. The combination of different pieces of evidence is laudable, but it can be done with statistical methods as well … This statistical approach has the advantage that it takes the parameter uncertainty into account: even if uncontroversial ‘deep parameters’ were available, they would have standard errors. Specification uncertainty makes things even worse. Negecting this leads to self-deception.
There are many kinds of useless economics held in high regard within mainstream economics establishment today. Few — if any — are less deserved than the macroeconomic theory/method — mostly connected with Nobel laureates Finn Kydland, Robert Lucas, Edward Prescott and Thomas Sargent — called calibration.
Hugo Keuzenkamp and yours truly are certainly not the only ones having doubts about the scientific value of calibration. In Journal of Economic Perspective (1996, vol. 10) Nobel laureates Lars Peter Hansen and James J. Heckman writes:
It is only under very special circumstances that a micro parameter such as the inter-temporal elasticity of substitution or even a marginal propensity to consume out of income can be ‘plugged into’ a representative consumer model to produce an empirically concordant aggregate model … What credibility should we attach to numbers produced from their ‘computational experiments’, and why should we use their ‘calibrated models’ as a basis for serious quantitative policy evaluation? … There is no filing cabinet full of robust micro estimats ready to use in calibrating dynamic stochastic equilibrium models … The justification for what is called ‘calibration’ is vague and confusing.
Mathematical statistician Aris Spanos — in Error and Inference (Mayo & Spanos, 2010, p. 240) — is no less critical:
Given that “calibration” purposefully foresakes error probabilities and provides no way to assess the reliability of inference, how does one assess the adequacy of the calibrated model? …
The idea that it should suffice that a theory “is not obscenely at variance with the data” (Sargent, 1976, p. 233) is to disregard the work that statistical inference can perform in favor of some discretional subjective appraisal … it hardly recommends itself as an empirical methodology that lives up to the standards of scientific objectivity
In physics it may possibly not be straining credulity too much to model processes as ergodic – where time and history do not really matter – but in social and historical sciences it is obviously ridiculous. If societies and economies were ergodic worlds, why do econometricians fervently discuss things such as structural breaks and regime shifts? That they do is an indication of the unrealisticness of treating open systems as analyzable with ergodic concepts.
The future is not reducible to a known set of prospects. It is not like sitting at the roulette table and calculating what the future outcomes of spinning the wheel will be. Reading Lucas, Sargent, Prescott, Kydland and other calibrationists one comes to think of Robert Clower’s apt remark that
much economics is so far removed from anything that remotely resembles the real world that it’s often difficult for economists to take their own subject seriously.
Instead of assuming calibration and rational expectations to be right, one ought to confront the hypothesis with the available evidence. It is not enough to construct models. Anyone can construct models. To be seriously interesting, models have to come with an aim. They have to have an intended use. If the intention of calibration and rational expectations is to help us explain real economies, it has to be evaluated from that perspective. A model or hypothesis without a specific applicability is not really deserving our interest.
To say, as Edward Prescott that
one can only test if some theory, whether it incorporates rational expectations or, for that matter, irrational expectations, is or is not consistent with observations
is not enough. Without strong evidence all kinds of absurd claims and nonsense may pretend to be science. We have to demand more of a justification than this rather watered-down version of “anything goes” when it comes to rationality postulates. If one proposes rational expectations one also has to support its underlying assumptions. None is given, which makes it rather puzzling how rational expectations has become the standard modeling assumption made in much of modern macroeconomics. Perhaps the reason is, as Paul Krugman has it, that economists often mistake
beauty, clad in impressive looking mathematics, for truth.
But I think Prescott’s view is also the reason why calibration economists are not particularly interested in empirical examinations of how real choices and decisions are made in real economies. In the hands of Lucas, Prescott and Sargent, rational expectations has been transformed from an – in principle – testable hypothesis to an irrefutable proposition. Believing in a set of irrefutable propositions may be comfortable – like religious convictions or ideological dogmas – but it is not science.