2011年3月11日星期五

补充一点笑料

可能会有人觉得上文只是一个看热闹的门外汉的个人偏见。那么他不妨看看做微观计量的学者是怎么调侃某些宏观经济学家的,比如兰小欢大帝敬仰的计量经济学家Arthur Goldberger:“有篇顶尖学报发表了一篇宏观经济学论文,我去数这个论文中所有的图和数字,好家伙,那总数比他的数据样本都大。”

应该需要学过一点统计才能理解笑点在哪里。不过没学过也没关系,可以看看MIT毕业的宏观经济学家Arnold Kling的说法,虽然他不像我的上篇文章那样没有节制:

In macroeconomics, there are more factors to be controlled for than there are observations. There are negative degrees of freedom, which should cause your statistical software to give you an error message.

Instead, the modeler limits the way that factors enter the model. For example, the modeler probably will not control for changes in the educational attainment of the labor force over time. That is not because the educational attainment over time does not matter. It is because the modeler does not want to put in so many factors that the computer spits out an error message.

There are thousands of ways to specify the "consumption function," which is the equation that predicts consumer spending. Should durable goods spending be separated from spending on nondurable goods and services? Should previous periods' income be used in addition to current income, and with what weight? Should a measure of anticipated future income be used? How should wealth enter the equation? Is there a way to account for the role of credit market conditions? How do tax considerations enter? Are there different propensities to consume out of wage income and out of transfer payments? How do consumers respond to changes in oil prices? How do they form expectations for oil prices in the future? What factors that are trending over time, such as population changes and shifts in the mix of consumption, need to be controlled for? Which time periods are affected by special factors, such as the recent snowstorms along the east coast?

If you have about 80 quarters of data to work with, and you have thousands of factors to control for, there is no conceivable way for the model's specification to reflect the data. Instead, the specification depends on the opinion of the modeler.

The conditions under which statistical techniques are scientifically valid are not satisfied with macroeconomic data. There is no reason to take model results as reflecting anything other than the opinion of the modeler.

What if the models performed well in out-of-sample forecasts? If that were the case, then I would have to concede that there might be some scientific validity to the models. However, that has never been the case. When I was a model jockey, the models were forever being tweaked with what were called "add factors" or "constant adjustments" in order to keep them on track with the most recent data. Formal studies of out-of-sample forecasts, by Stephen McNees of the Boston Fed and others, showed dismal performance. Even today, the models that are telling us how many jobs the stimulus saved are the same models that predicted that unemployment today would be close to 7 percent with the stimulus, when in reality it is 9.7 percent. So out-of-sample performance fails to boost one's confidence in the scientific status of these models.

Macroeconometric models satisfy a deep need to create the illusion that government can exercise precise control over output and employment. As long as people are determined to believe that such control is possible, the models will have a constituency. For better or worse.

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