Using Implied Probabilities to Improve Estimation with Unconditional Moment Restrictions

Alain Guay and Florian Pelgrin

Cahiers de recherche from CIRPEE

Abstract: In this paper, we investigate the information content of implied probabilities (Back and Brown, 1993) to improve estimation in unconditional moment conditions models. We propose and evaluate two 3-step euclidian empirical likelihood estimators and their bias-correction versions for weakly dependent data. The first one is the time series extension of the 3S-EEL proposed by Antoine, Bonnal and Renault (2007).The second one is new and uses in contrast only an estimator of the weighting matrix at an efficient 2-step GMM estimator, while leaving unrestricted the Jacobian matrix. Both estimators use implied probabilities to achieve higher-order improvements relative to the traditional GMM estimator. A Monte-Carlo study reveals that the finite and large sample properties of the (bias-corrected) 3-step estimators compare very favorably to the existing approaches: the 2-step GMM and the continuous updating estimator. As an application, we re-assess the empirical evidence regarding the New Keynesian Phillips curve in the US.

Keywords: Information-based inference; Implied probabilities; Weak identification; Generalized method of moments; Philips curve (search for similar items in EconPapers)
JEL-codes: C13 C14 E31 (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-ecm and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:lvl:lacicr:0747

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