by Summary:
Although it might be possible to find dissenters, the apparent consensus among financial market practitioners is that mathematical economic models provide terrible forecasts. One response is to keep searching through the set of all possible models, hoping to find something that works. The author's suggested response is to accept that forecasting is an inherently impossible task. However, in order to advance beyond nihilism, we need to quantify why mathematical models are terrible. My argument is straightforward: the models that provide the best fit to observed behaviour cannot themselves be forecast with the type of information that we have available in the real world. (As an aside, I would note that Beatrice Cherrier has written about the preference for tractable (simplicity) of
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Mike Norman considers the following as important: economic forecasting, economic modeling
This could be interesting, too:
Although it might be possible to find dissenters, the apparent consensus among financial market practitioners is that mathematical economic models provide terrible forecasts. One response is to keep searching through the set of all possible models, hoping to find something that works. The author's suggested response is to accept that forecasting is an inherently impossible task. However, in order to advance beyond nihilism, we need to quantify why mathematical models are terrible. My argument is straightforward: the models that provide the best fit to observed behaviour cannot themselves be forecast with the type of information that we have available in the real world. (As an aside, I would note that Beatrice Cherrier has written about the preference for tractable (simplicity) of
Topics:
Mike Norman considers the following as important: economic forecasting, economic modeling
This could be interesting, too:
Mike Norman writes Lars P. Syll — Economics — too important to be left to economists
Mike Norman writes Lars P. Syll — Does it–really–take a model to beat a model?
Mike Norman writes A spreadsheet version of the IS/MY model (alternative to IS/LM model) — Dirk Ehnts
Mike Norman writes Econometrics and the problem of unjustified assumptions — Lars P. Syll
Although it might be possible to find dissenters, the apparent consensus among financial market practitioners is that mathematical economic models provide terrible forecasts. One response is to keep searching through the set of all possible models, hoping to find something that works. The author's suggested response is to accept that forecasting is an inherently impossible task. However, in order to advance beyond nihilism, we need to quantify why mathematical models are terrible. My argument is straightforward: the models that provide the best fit to observed behaviour cannot themselves be forecast with the type of information that we have available in the real world.
(As an aside, I would note that Beatrice Cherrier has written about the preference for tractable (simplicity) of mathematical models in "What is the Cost of 'Tractable' Economic Models," and in a follow up article. Much of what she discusses appears to overlap my thinking about the existing methodology, although I believe that I have a different view. My suggestion is to look at non-forecastable economic models, and tractable (or reduced order) non-forecastable models would be the most interesting. A non-tractable non-forecastable model might provide the best fit to reality, but its complexity would also make it difficult to draw any conclusions from it; this is essentially my concern with agent-based models. Since I was in the middle of laying out my logic, I was not able to work in a longer comparison to her arguments.)Most economic models are either gadgets or thinking tools that assist in adding rigor. They are not capable of predicting the future any more than sociological models are.
For one thing, there are too many contingent factors involved, even if all were known and could be specified with some degree of precision based on data.
It is not possible to generate an economic ephemeris. No economic laws of motion have been forthcoming.
The question then becomes how close is is possible to comes, e.g., what is the degree of error. Even that can only be estimated.
The multiplicity of sources of disturbances makes a generalisation of the notion of "small disturbances" difficult. As a result, I would argue that we should instead worry about analysing models with a focus on the properties of their forecast errors rather than the precise nature of the generalisation of exact forecastability....
Brian Romanchuk