Bayesian and Frequentist Inference in Partially Identified Models

By H. R. Moon, Frank Schorfheide. 2009

190 citations recorded by the source corpus.

Subfields: Political Science and International Relations, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/27e6e51c043e945dd43ef66c43671d2e78aa9095

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In Political Science and International Relations

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Citations in the corpus

Bayesian inference in a class of partially identified models (2016, background): “This sort of approach is mentioned in Moon and Schorfheide (2009).”
DISENTANGLING DEMAND AND SUPPLY SHOCKS IN THE CRUDE OIL MARKET: HOW TO CHECK SIGN RESTRICTIONS IN STRUCTURAL VARS (2012, unknown): “Although such arguments are valid, it may be worth noting that sign restrictions can be used in conjunction with classical estimation methods (see Moon and Schorfheide (2009) and Moon, Schorfheide, Granziera and Lee (2009) for a discussion of the frequentist approach to inference in such models).”
Why Agnostic Sign Restrictions are not Enough: Understanding the Dynamics of Oil Market VAR Models (2009, background): “Unlike the first generation of exactly identified oil market VAR models, these structural VAR models are only partially identified - in the sense discussed in Moon and Schorfheide (2009) - and allow for a wide range of different estimates.”
Semiparametric Inference in Dynamic Binary Choice Models, Second Version (2014, background): “See Eckstein and Wolpin (1989), Rust (1994), Pakes (1994), Miller (1997), Aguirregabiria and Mira (2010) and Keane, Todd, and Wolpin (2011) for surveys of the literature.”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, background): “They provide conditions under which the robust credible set converges to the identified set if the sample is large (thereby avoiding the Moon and Schorfheide (2012) critique).”
Business cycle measurement with some theory (2011, unknown): “The experiments also showed that credible intervals tend to be large—this is expected given that the methodology delivers partially identified empirical models (see Moon and Schorfheide, 2009).”
Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance (2013, methodology): “Works considering Bayesian interval estimators in partially identified models include Moon and Schorfeide (2012), Liao and Jiang (2010) and Gustafson and Greenland (2009).”