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Identification and Inference in Linear Stochastic Discount Factor Models with Excess Returns

  • Craig Burnside

When excess returns are used to estimate linear stochastic discount factor (SDF) models, researchers often adopt a normalization of the SDF that sets its mean to 1, or one that sets its intercept to 1. These normalizations are often treated as equivalent, but they are subtly different both in population, and in finite samples. Standard asymptotic inference relies on rank conditions that differ across the two normalizations, and which can fail to differing degrees. I first establish that failure of the rank conditions is a genuine concern for many well known SDF models in the literature. I also describe how failure of the rank conditions can affect inference, both in population and in finite samples. I propose using tests of the rank conditions not only as a diagnostic device, but also for model reduction. I show that this model reduction procedure has desirable size and power properties in a Monte Carlo experiment with a calibrated model.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 16634.

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Date of creation: Dec 2010
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Handle: RePEc:nbr:nberwo:16634
Note: AP EFG
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  1. Jay Shanken & Guofu Zhou, 2006. "Estimating and Testing Beta Pricing Models: Alternative Methods and their Performance in Simulations," NBER Working Papers 12055, National Bureau of Economic Research, Inc.
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  9. Wright, Jonathan H., 2003. "Detecting Lack Of Identification In Gmm," Econometric Theory, Cambridge University Press, vol. 19(02), pages 322-330, April.
  10. Ferson, Wayne E & Harvey, Campbell R, 1993. "The Risk and Predictability of International Equity Returns," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 527-66.
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