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Measurement error in nonlinear models - a review

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  • Susanne M. Schennach

    () (Institute for Fiscal Studies and Brown University)

Abstract

This overview of the recent econometrics literature on measurement error in nonlinear models centres on the question of the identification and estimation of general nonlinear models with measurement error. Simple approaches that rely on distributional knowledge regarding the measurement error (such as deconvolution or validation data techniques) are briefly presented. Then follows a description of methods that secure identification via more readily available auxiliary variables (such as repeated measurements, measurement systems with a 'factor model' structure, instrumental variables and panel data). Methods exploiting higher-order moments or bounding techniques to avoid the need for auxiliary information are presented next. Special attention is devoted to a recently introduced general method to handle a broad class of latent variable models, called Entropic Latent Variable Integration via Simulation (ELVIS). Finally, the complex but active topic of nonclassical measurement error is covered and applications of measurement error techniques to other fields are outlined.

Suggested Citation

  • Susanne M. Schennach, 2012. "Measurement error in nonlinear models - a review," CeMMAP working papers CWP41/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:41/12
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    File URL: http://www.cemmap.ac.uk/wps/cwp411212.pdf
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    2. Stefan Hoderlein & Bettina Siflinger & Joachim Winter, 2015. "Identification of structural models in the presence of measurement error due to rounding in survey responses," Boston College Working Papers in Economics 869, Boston College Department of Economics.
    3. repec:wly:emjrnl:v:19:y:2016:i:3:p:c95-c127 is not listed on IDEAS
    4. Takahide Yanagi, 2014. "The Effect of Measurement Error in the Sharp Regression Discontinuity Design," KIER Working Papers 910, Kyoto University, Institute of Economic Research.
    5. repec:wly:jmoncb:v:49:y:2017:i:1:p:115-169 is not listed on IDEAS
    6. Dongwoo Kim & Daniel Wilhelm, 2017. "Powerful t-Tests in the presence of nonclassical measurement error," CeMMAP working papers CWP57/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Drerup, Tilman & Enke, Benjamin & Gaudecker, Hans-Martin von, 2014. "Measurement Error in Subjective Expectations and the Empirical Content of Economic Models," IZA Discussion Papers 8535, Institute for the Study of Labor (IZA).
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    10. Karun Adusumilli & Taisuke Otsu, 2015. "Nonparametric instrumental regression with errors in variables," STICERD - Econometrics Paper Series /2015/585, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    11. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    12. Naoki Wakamori & Angelika Welte, 2017. "Why Do Shoppers Use Cash? Evidence from Shopping Diary Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(1), pages 115-169, February.
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    14. Susanne M. Schennach, 2013. "Convolution without independence," CeMMAP working papers CWP46/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. YANAGI, Takahide, 2017. "Inference on Local Average Treatment Effects for Misclassified Treatment," Discussion Papers 2017-02, Graduate School of Economics, Hitotsubashi University.
    16. repec:cup:etheor:v:34:y:2018:i:01:p:134-165_00 is not listed on IDEAS
    17. 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.

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