IDEAS home Printed from https://ideas.repec.org/p/hhs/osloec/1998_006.html
   My bibliography  Save this paper

Deriving Bounds on the Structural Vector when the Measurement Errors are Correlated: An Elaboration of the Frisch/Reiersol Approach

Author

Listed:
  • Willasen, Y.

Abstract

In the general linear errors-in-variables model the main results have been derived under the assuption that the measurement errors are uncorrelated. However, as recognized by Bekker, Kapteyn and Wansbeek (BKW) (1997) and Lach (1993) this is often a problematic assumption to maintain in empirical applications since quite trivial variable transformations will often create correlation between the errors.

Suggested Citation

  • Willasen, Y., 1998. "Deriving Bounds on the Structural Vector when the Measurement Errors are Correlated: An Elaboration of the Frisch/Reiersol Approach," Memorandum 06/1998, Oslo University, Department of Economics.
  • Handle: RePEc:hhs:osloec:1998_006
    as

    Download full text from publisher

    File URL: https://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/1998/Memo-06-1998.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Klepper, Steven & Leamer, Edward E, 1984. "Consistent Sets of Estimates for Regressions with Errors in All Variables," Econometrica, Econometric Society, vol. 52(1), pages 163-183, January.
    2. Lach, Saul, 1993. "Decomposition of Variables and Correlated Measurement Errors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 34(3), pages 715-725, August.
    3. Erickson, Timothy, 1993. "Restricting Regression Slopes in the Errors-in-Variables Model by Bounding the Error Correlation," Econometrica, Econometric Society, vol. 61(4), pages 959-969, July.
    4. Bekker, Paul & Kapteyn, Arie & Wansbeek, Tom, 1987. "Consistent Sets of Estimates for Regressions with Correlated or Uncorrelated Measurement Errors in Arbitrary Subsets of All Variables," Econometrica, Econometric Society, vol. 55(5), pages 1223-1230, September.
    5. Moran, P. A. P., 1971. "Estimating structural and functional relationships," Journal of Multivariate Analysis, Elsevier, vol. 1(2), pages 232-255, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kishore Gawande & Alok K. Bohara, 2000. "Errors‐in‐Variables Bounds in a Tobit Model of Endogenous Protection," Southern Economic Journal, John Wiley & Sons, vol. 66(4), pages 881-905, April.
    2. Hu, Yingyao, 2006. "Bounding parameters in a linear regression model with a mismeasured regressor using additional information," Journal of Econometrics, Elsevier, vol. 133(1), pages 51-70, July.
    3. Erik Biørn, 2000. "Panel Data With Measurement Errors: Instrumental Variables And Gmm Procedures Combining Levels And Differences," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 391-424.
    4. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    5. Stoker, Thomas M. & Berndt, Ernst R. & Denny Ellerman, A. & Schennach, Susanne M., 2005. "Panel data analysis of U.S. coal productivity," Journal of Econometrics, Elsevier, vol. 127(2), pages 131-164, August.
    6. Jonathan Temple, 1995. "Testing the augmented Solow Model," Economics Papers 18 & 106., Economics Group, Nuffield College, University of Oxford.
    7. Erik Biørn, 2002. "Handling the measurement error problem by means of panel data: Moment methods applied on firm data," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B6-1, International Conferences on Panel Data.
    8. Verdugo, Gregory, 2016. "Real wage cyclicality in the Eurozone before and during the Great Recession: Evidence from micro data," European Economic Review, Elsevier, vol. 82(C), pages 46-69.
    9. Francis J. DiTraglia & Camilo Garcia-Jimeno, 2020. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," Papers 2011.07276, arXiv.org.
    10. Damian Clarke & Benjamín Matta, 2018. "Practical considerations for questionable IVs," Stata Journal, StataCorp LP, vol. 18(3), pages 663-691, September.
    11. Seoyun Hong & Chang Sik Kim & Hyunchul Kim, 2022. "Measuring the Effects of Bid-Rigging on Prices with Binary Misclassification," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 61(3), pages 319-339, November.
    12. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    13. Magnac, Thierry, 2013. "Identification partielle : méthodes et conséquences pour les applications empiriques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 233-258, Décembre.
    14. Christian Bontemps & Thierry Magnac, 2017. "Set Identification, Moment Restrictions, and Inference," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 103-129, September.
    15. Susanne M. Schennach, 2012. "Measurement error in nonlinear models - a review," CeMMAP working papers 41/12, Institute for Fiscal Studies.
    16. Erickson, Timothy & Whited, Toni M., 2005. "Proxy-quality thresholds: Theory and applications," Finance Research Letters, Elsevier, vol. 2(3), pages 131-151, September.
    17. Shalabh, 1998. "Improved Estimation in Measurement Error Models Through Stein Rule Procedure," Journal of Multivariate Analysis, Elsevier, vol. 67(1), pages 35-48, October.
    18. Greenaway, David & Torstensson, Johan, 2000. "Economic Geography, Comparative Advantage and Trade within Industries: Evidence from the OECD," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 15, pages 260-280.
    19. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Sociological Methods & Research, , vol. 46(3), pages 303-341, August.
    20. Jean-Pierre Florens & Anna Simoni, 2021. "Revisiting Identification Concepts in Bayesian Analysis," Annals of Economics and Statistics, GENES, issue 144, pages 1-38.

    More about this item

    Keywords

    ECONOMETRICS ; MODELS ; MEASUREMENT;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:osloec:1998_006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mari Strønstad Øverås (email available below). General contact details of provider: https://edirc.repec.org/data/souiono.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.