Resolving the Errors-in-Variables Bias in Risk Premium Estimation

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The Fama-Macbeth (1973) rolling-B method is widely used for estimating risk premiums, but its inherent errors-in-variables bias remains an unresolved problem, particularly when using individual assets or macroeconomic factors.  We propose a solution with a particular instrumental variable, B calculated from alternate observations.  The resulting estimators are unbiased.  In simulations, we compare this new approach with several existing methods.  The new approach corrects the bias even when the sample period is limited.  Moreover, our proposed standard errors are unbiased, and lead to correct rejection size in finite samples.


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