Entropic Latent Variable Integration via Simulation
This paper introduces a general method to convert a model defined by moment conditions that involve both observed and unobserved variables into equivalent moment conditions that involve only observable variables. This task can be accomplished without introducing infinite‐dimensional nuisance parameters using a least favorable entropy‐maximizing distribution. We demonstrate, through examples and simulations, that this approach covers a wide class of latent variables models, including some game‐theoretic models and models with limited dependent variables, interval‐valued data, errors‐in‐variables, or combinations thereof. Both point‐ and set‐identified models are transparently covered. In the latter case, the method also complements the recent literature on generic set‐inference methods by providing the moment conditions needed to construct a generalized method of moments‐type objective function for a wide class of models. Extensions of the method that cover conditional moments, independence restrictions, and some state‐space models are also given.
Volume (Year): 82 (2014)
Issue (Month): 1 (01)
|Contact details of provider:|| Phone: 1 212 998 3820|
Fax: 1 212 995 4487
Web page: http://www.econometricsociety.org/
More information through EDIRC
|Order Information:|| Web: https://www.econometricsociety.org/publications/econometrica/access/ordering-back-issues Email: |
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.:
- Charles F. Manski & Elie Tamer, 2002. "Inference on Regressions with Interval Data on a Regressor or Outcome," Econometrica, Econometric Society, vol. 70(2), pages 519-546, March.
- S. M. Schennach & Yingyao Hu, 2013.
"Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information,"
Journal of the American Statistical Association,
Taylor & Francis Journals, vol. 108(501), pages 177-186, March.
- Susanne M. Schennach & Yingyao Hu, 2012. "Nonparametric identification and semiparametric estimation of classical measurement error models without side information," CeMMAP working papers CWP40/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Gerda Claeskens, 2004. "Restricted likelihood ratio lack-of-fit tests using mixed spline models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 909-926.
- Yuichi Kitamura, 2001. "Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions," Econometrica, Econometric Society, vol. 69(6), pages 1661-1672, November.
- Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
- Susanne M Schennach, 2007. "Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models," Econometrica, Econometric Society, vol. 75(1), pages 201-239, 01.
- Susanne M. Schennach, 2004. "Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models," Econometric Society 2004 North American Summer Meetings 602, Econometric Society.
- Canay, Ivan A., 2010. "EL inference for partially identified models: Large deviations optimality and bootstrap validity," Journal of Econometrics, Elsevier, vol. 156(2), pages 408-425, June. Full references (including those not matched with items on IDEAS)