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Estimation of nonlinear errors-in-variables models: an approximate solution

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Abstract

We propose an easy to derive and simple to compute approximate least squares or maximum likelihood estimator for nonlinear errors-in-variables models that does not require the knowledge of the conditional density of the latent variables given the observables. Specific examples and Monte Carlo studies demonstrate that the bias of this approximate estimator is small even when the magnitude of the variance of measurement errors to the variance of measured covariates is large.

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Authors and Affiliations

  1. Department of Economics, University of Southern California, 90089-0253, Los Angeles, CA, USA

    C. Hsiao

  2. Graduate School of Business, University of Chicago, 60637, Chicago, IL, USA

    L. Wang

  3. Institute for Statistics and Econometrics, University of Basel, Holbeinstrasse 12, CH-4051, Basel, Switzerland

    Q. Wang

Authors
  1. C. Hsiao
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  2. L. Wang
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  3. Q. Wang
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Additional information

Cheng Hsiao and Qing Wang's work was supported in part by National Science Foundation grant SeS91-22481 and SBR94-09540. Liqun Wang gratefully acknowledges the financial support from Swiss National Science Foundation. We wish to thank Professor H. Schneeweiss and a referee for helpful comments and suggestions.

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Hsiao, C., Wang, L. & Wang, Q. Estimation of nonlinear errors-in-variables models: an approximate solution. Statistical Papers 38, 1–25 (1997). https://doi.org/10.1007/BF02925212

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  • DOI: https://doi.org/10.1007/BF02925212

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