Well-Posedness of Measurement Error Models for Self-Reported Data
AbstractIt is widely admitted that the inverse problem of estimating the distribution of a latent variable X* from an observed sample of X, a contaminated measurement of X*, is ill-posed. This paper shows that a property of self-reporting errors, observed from validation studies, is that the probability of reporting the truth is nonzero conditional on the true values, and furthermore, this property implies that measurement error models for self-reporting data are in fact well-posed. We also illustrate that the classical measurement error models may in fact be conditionally well-posed given prior information on the distribution of the latent variable X*.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 556.
Date of creation: Oct 2009
Date of revision:
Other versions of this item:
- An, Yonghong & Hu, Yingyao, 2012. "Well-posedness of measurement error models for self-reported data," Journal of Econometrics, Elsevier, vol. 168(2), pages 259-269.
- Yonghong An & Yingyao Hu, 2009. "Well-posedness of measurement error models for self-reported data," CeMMAP working papers CWP35/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
This paper has been announced in the following NEP Reports:
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.:
- Xiaohong Chen & Han Hong & Alessandro Tarozzi, 2008.
"Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects,"
Cowles Foundation Discussion Papers
1644, Cowles Foundation for Research in Economics, Yale University.
- Chen, Xiaohong & Hong, Han & Tarozzi, Alessandro, 2008. "Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects," Working Papers 42, Yale University, Department of Economics.
- Li, Tong, 2002. "Robust and consistent estimation of nonlinear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 110(1), pages 1-26, September.
- Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, 09.
- Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
- Xiaohong Chen & Markus Reiss, 2007.
"On Rate Optimality for Ill-posed Inverse Problems in Econometrics,"
Cowles Foundation Discussion Papers
1626, Cowles Foundation for Research in Economics, Yale University.
- Chen, Xiaohong & Reiss, Markus, 2011. "On Rate Optimality For Ill-Posed Inverse Problems In Econometrics," Econometric Theory, Cambridge University Press, vol. 27(03), pages 497-521, June.
- Xiaohong Chen & Markus Reiss, 2007. "On rate optimality for ill-posed inverse problems in econometrics," CeMMAP working papers CWP20/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," Review of Economic Studies, Oxford University Press, vol. 72(2), pages 343-366.
- Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2007. "Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves," Econometrica, Econometric Society, vol. 75(6), pages 1613-1669, November.
- Bound, John & Krueger, Alan B, 1991.
"The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?,"
Journal of Labor Economics,
University of Chicago Press, vol. 9(1), pages 1-24, January.
- John Bound & Alan B. Krueger, 1989. "The Extent of Measurement Error In Longitudinal Earnings Data: Do Two Wrongs Make A Right?," NBER Working Papers 2885, National Bureau of Economic Research, Inc.
- Hesse, C. H., 1995. "Deconvolving a Density from Partially Contaminated Observations," Journal of Multivariate Analysis, Elsevier, vol. 55(2), pages 246-260, November.
- Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2003. "Nonparametric IV estimation of shape-invariant Engel curves," CeMMAP working papers CWP15/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Yingyao Hu & Geert Ridder, 2010.
"On Deconvolution as a First Stage Nonparametric Estimator,"
Taylor & Francis Journals, vol. 29(4), pages 365-396.
- Yingyao Hu & Geert Ridder, 2005. "On Deconvolution as a First Stage Nonparametric Estimator," IEPR Working Papers 05.29, Institute of Economic Policy Research (IEPR).
- Li, Tong & Vuong, Quang, 1998. "Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 139-165, May.
- Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-94, July.
- Rohman, Ibrahim Kholilul & Bohlin, Erik, 2011. "Towards the alternative measurement: Discovering the relationships between technology adoption and quality of life in Indonesia," 22nd European Regional ITS Conference, Budapest 2011: Innovative ICT Applications - Emerging Regulatory, Economic and Policy Issues 52206, International Telecommunications Society (ITS).
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (None) The email address of this maintainer does not seem to be valid anymore. Please ask None to update the entry or send us the correct address.
If references are entirely missing, you can add them using this form.