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Applied Econometrics and Asset Pricing最新文献

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Orthogonalized Regressors and Spurious Precision 正交回归量与伪精度
Pub Date : 2012-09-01 DOI: 10.2139/ssrn.2140191
P. Sercu, M. Vandebroek
Regressions often use pre-orthogonalized regressors. For example, the exposure of a stock's return to exchange-rate changes is conventionally estimated by regression, and often, the market return is included as an additional regressor. By first orthogonalizing the market return on the exchange rate one seems to have the best of both worlds: the market factor cannot subsume part of the exposure present in a stock's return, and the SE of the estimate beats both the simple- and the multiple-regression SE's. This last effect is illusory: since the simple and the pseudo-multiple regression always produce the same exposure estimate, given the sample, their precision must be identical too. Technically, the source of the problem is that the uncertainty about the market's exposure estimate is left out of the calculated SE. In published work, the calculated error variances should be corrected upward by 20 to 100 percent.
回归通常使用预正交回归量。例如,股票回报对汇率变化的影响通常是通过回归来估计的,通常,市场回报被作为一个额外的回归因子包括在内。通过首先将市场回报与汇率正交化,人们似乎可以两全其美:市场因素不能包含股票回报中存在的部分风险,并且估计的SE优于单一回归和多元回归SE。最后的效果是虚幻的:由于简单回归和伪多元回归总是产生相同的暴露估计,给定样本,它们的精度也必须相同。从技术上讲,问题的根源在于计算出的SE中没有考虑市场敞口估计的不确定性。在已发表的作品中,计算出的误差方差应向上修正20%至100%。
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引用次数: 6
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Applied Econometrics and Asset Pricing
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