Identification and inference in two-pass asset pricing models |
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| Affiliation: | 1. Development Prospects Group, World Bank, USA;2. Brookings Institution, USA;3. CEPR, UK;1. University of Warwick, Coventry CV4 7AL, UK;2. University of Nottingham, UK;3. University of Melbourne, Australia;1. Development Prospects Group-World Bank, United States;2. Brookings Institution, United States;3. CEPR, United Kingdom;1. TU Delft, Delft Institute of Applied Mathematics, HB 03.270, Mekelweg 4, 2628 CD Delft, The Netherlands;2. CWI-Centrum Wiskunde & Informatica, Amsterdam, The Netherlands;1. Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, United States of America;2. Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, United States of America;1. European Central Bank, 60640 Frankfurt am Main, Germany;2. Center for Economic Studies, KU Leuven, Naamsestraat 69, 3000 Leuven, Belgium;3. Department of Economics, University of Foggia, 71100 Foggia, Italy |
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| Abstract: | We introduce a framework that robustifies two-pass Fama–MacBeth regressions, in the sense that confidence regions for the ex post price of risk can be derived reliably even with weak identification. This region can be unbounded, if risk price is hard to identify, empty, if the model lacks fit, and bounded otherwise. Our framework thus provides automatic weak-identification and lack-of-fit warnings, and informative model rejections. Empirically relevant simulations document attractive size and power properties. Empirical applications with well known models and data sets illustrate practical usefulness and the potential value of additional cross-sectional information. |
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| Keywords: | Cross-sectional asset pricing inference Fama–MacBeth Weak identification Reduced rank beta CAPM Fama–French factors |
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