27
Apr
10

Pronósticos, una tentación a promediar

Vox tiene un articulo clave sobre pronósticos económicos y metodología econométrica (y supuestos). Haciendo hincapié en la disyuntiva entre promediar modelos o partir de un modelo general hacia uno más especifico.

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The exchange emphasises not just that economic variables are important in forecasts, but that econometric issues matter. If GDP is trend stationary, the implications are very different for forecasting than if GDP is a random walk with drift – one will correct to some equilibrium, the other won’t. Economic nuances matter too – what other variables make up this equilibrium relationship? Historically, there has been such a steady state, but whether that is the same one to which we will soon correct is unclear, and bad forecasts may result.

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Averaging forecasts is motivated by the perceived difficulty of choosing a single model. It is often not clear in advance which model will forecast best, thus we take the insurance of averaging over a number of models. We forecast worse than the best model (the premium), but we forecast better than the worst model (the pay out) by smoothing forecast errors.
However, is this a sensible strategy? Do we do better by averaging? The alternative to averaging forecasts is to select one particular forecast, but framed in the context of 2K possible models for K variables in a dataset, choosing the “right” model seems an impossible task when K is at all large.

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Hendry (1995) emphasised a general-to-specific strategy of starting with the most general empirical model possible. This general model would incorporate variables from all economic theory and previous econometric work. It should also be well specified, satisfying the assumptions placed on the statistical model. From there, a search would begin for the simplest model that still satisfied the statistical assumptions.

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Por ultimo, en el post se trata el tema de mercados predictivos como una forma de promediar modelos (Ejemplo: Intrade).

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Prediction markets, particularly on frequent similar events such as soccer matches, provide an excellent natural “simulation” study to determine forecast accuracy. By considering thousands of sports matches, we can ascertain the accuracy of prediction markets.

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Of course, trusting prediction markets to forecast well in the future relies on the assumption that the event they are predicting is stationary; that soccer matches do not suddenly change in their nature overnight – the goals aren’t widened or the rules drastically altered. In that non-stationary situation, it would be impossible to know whether InTrade would continue to forecast well.

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Al final de la nota hay una completa lista de bibliográfica sobre el tema.

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