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Estimation with overidentifying inequality moment conditions

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  • Moon, Hyungsik Roger
  • Schorfheide, Frank

Abstract

This paper derives limit distributions of empirical likelihood estimators for models in which inequality moment conditions provide overidentifying information. We show that the use of this information leads to a reduction of the asymptotic mean-squared estimation error and propose asymptotically uniformly valid tests and confidence sets for the parameters of interest. While inequality moment conditions arise in many important economic models, we use a dynamic macroeconomic model as a data generating process and illustrate our methods with instrumental variable estimators of monetary policy rules. The results obtained in this paper extend to conventional GMM estimators.

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  • Moon, Hyungsik Roger & Schorfheide, Frank, 2009. "Estimation with overidentifying inequality moment conditions," Journal of Econometrics, Elsevier, vol. 153(2), pages 136-154, December.
  • Handle: RePEc:eee:econom:v:153:y:2009:i:2:p:136-154
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Battey, Heather & Feng, Qiang & Smith, Richard J., 2016. "Improving confidence set estimation when parameters are weakly identified," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 117-123.
    2. Chaudhuri, Saraswata & Zivot, Eric, 2011. "A new method of projection-based inference in GMM with weakly identified nuisance parameters," Journal of Econometrics, Elsevier, vol. 164(2), pages 239-251, October.
    3. João Madeira & Nuno Palma, 2018. "Measuring monetary policy deviations from the Taylor rule," The School of Economics Discussion Paper Series 1803, Economics, The University of Manchester.
    4. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.
    5. Kleiber Christian & Zeileis Achim, 2013. "Reproducible Econometric Simulations," Journal of Econometric Methods, De Gruyter, vol. 2(1), pages 89-99, July.
    6. Alastair R. Hall, 2013. "Generalized Method of Moments," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 14, pages 313-333 Edward Elgar Publishing.
    7. Andrew J. Patton & Allan Timmermann, 2011. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17, June.
    8. He, Yinghua, 2012. "Gaming the Boston School Choice Mechanism in Beijing," TSE Working Papers 12-345, Toulouse School of Economics (TSE).
    9. Shi, Xiaoxia, 2015. "Model selection tests for moment inequality models," Journal of Econometrics, Elsevier, vol. 187(1), pages 1-17.
    10. Nicky L. Grant & Richard J. Smith, 2018. "GEL-Based Inference from Unconditional Moment Inequality Restrictions," The School of Economics Discussion Paper Series 1802, Economics, The University of Manchester.
    11. Coroneo, Laura & Corradi, Valentina & Santos Monteiro, Paulo, 2012. "Testing for optimal monetary policy via moment inequalities," The Warwick Economics Research Paper Series (TWERPS) 985, University of Warwick, Department of Economics.
    12. Francis DiTraglia & Camilo García-Jimeno, 2016. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," NBER Working Papers 22621, National Bureau of Economic Research, Inc.
    13. Ruoyao Shi & Zhipeng Liao, 2018. "An Averaging GMM Estimator Robust to Misspecification," Working Papers 201803, University of California at Riverside, Department of Economics.
    14. Francis J. DiTraglia & Camilo García-Jimeno, 2017. "Mis-classified, Binary, Endogenous Regressors: Identification and Inference," NBER Working Papers 23814, National Bureau of Economic Research, Inc.
    15. Mardi Dungey & Vitali Alexeev & Jing Tian & Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91, pages 1-24, June.

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