Identification and inference in linear stochastic discount factor models
By: Craig Burnside
Contributors: National Bureau of Economic Research
Series: NBER working paper series -- working paper 16634; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 16634.
Publisher: National Bureau of Economic Research
Published: 2010
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Description: "When linear asset pricing models are estimated using excess return data, a normalization of the model must be selected.  Several normalizations are equivalent when the model is correctly specified, but the identification conditions differ across normalizations.  In practice, some or all of these identification conditions fail statistically when conventional consumption-based models are estimated, and inference is not robust across normalizations.  Using asymptotic theory and Monte Carlo simulations, I present evidence that the lack of robustness in qualitative inference across normalizations can be attributed to model misspecification and lack of identification.  I propose the use of tests for failure of the rank conditions.  Using a calibrated model, I show that these tests are effective in detecting non-identified models"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 4/7/2011.

Includes bibliographical references.

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Mode of access: World Wide Web.