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https://hdl.handle.net/10356/98269
Title: | Measurement errors and censored structural latent variables models | Authors: | Chen, Songnian. Hsiao, Cheng. Wang, Liqun. |
Issue Date: | 2011 | Source: | Chen, S., Hsiao, C., & Wang, L. (2012). Measurement Errors and Censored Structural Latent Variables Models. Econometric Theory, 28(03), 696-703. | Series/Report no.: | Econometric theory | Abstract: | We consider censored structural latent variables models where some exogenous variables are subject to additive measurement errors. We demonstrate that overidentification conditions can be exploited to provide natural instruments for the variables measured with errors, and we propose a two-stage estimation procedure. The first stage involves substituting available instruments in lieu of the variables that are measured with errors and estimating the resulting reduced form parameters using consistent censored regression methods. The second stage obtains structural form parameters using the conventional linear simultaneous equations model estimators. | URI: | https://hdl.handle.net/10356/98269 http://hdl.handle.net/10220/13246 |
DOI: | 10.1017/S0266466611000715 | Schools: | School of Humanities and Social Sciences | Rights: | © 2011 Cambridge University Press. This paper was published in Econometric Theory and is made available as an electronic reprint (preprint) with permission of Cambridge University Press. The paper can be found at the following official DOI: [http://dx.doi.org/10.1017/S0266466611000715]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | HSS Journal Articles |
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Measurement errors and censored structural latent variables models.pdf | 74.79 kB | Adobe PDF | View/Open |
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