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Efficient information theoretic inference for conditional moment restrictions Author info | Abstract | Publisher info | Download info | Related research | Statistics Richard Smith () (Institute for Fiscal Studies and University of Cambridge)
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The generalized method of moments estimator may be substantially biased in finite samples, especially so when there are large numbers of unconditional moment conditions. This paper develops a class of first order equivalent semi-parametric efficient estimators and tests for conditional moment restrictions models based on a local or kernel-weighted version of the Cressie-Read power divergence family of discrepancies. This approach is similar in spirit to the empirical likelihood methods of Kitamura, Tripathi and Ahn (2004) and Tripathi and Kitamura (2003). These efficient local methods avoid the necessity of explicit estimation of the conditional Jacobian and variance matrices of the conditional moment restrictions and provide empirical conditional probabilities for the observations.
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number
CWP14/05.
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Length: 40 pp.
Date of creation: Oct 2005Date of revision:
Handle: RePEc:ifs:cemmap:14/05Contact details of provider: Postal: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE Phone: (+44) 020 7291 4800 Fax: (+44) 020 7323 4780 Email: Web page: http://cemmap.ifs.org.uk
Order Information: Postal: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE Email:
For technical questions regarding this item, or to correct its listing, contact: (Emma Hyman).
Keywords: Conditional Moment Restrictions ; Local Cressie-Read Minimum Discrepancy ; GMM ; Semi-Parametric Efficiency ; Other versions of this item:
Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
This paper has been announced in the following NEP Reports :
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996.
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CeMMAP working papers
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[Downloadable!]
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