IDEAS home Printed from https://ideas.repec.org/a/bla/obuest/v78y2016i1p113-125.html
   My bibliography  Save this article

A Simple Improvement of the IV-estimator for the Classical Errors-in-Variables Problem

Author

Listed:
  • Jonas Andersson
  • Jarle Møen

Abstract

Two measures of an error-ridden explanatory variable make it possible to solve the classical errors-in-variable problem by using one measure as an instrument for the other. It is well known that a second IV estimate can be obtained by reversing the roles of the two measures. We explore a simple estimator that is the linear combination of these two estimates, that minimizes the asymptotic mean squared error. In a Monte Carlo study we show that the gain in precision is significant compared to using only one of the original IV estimates. The proposed estimator also compares well with full information maximum likelihood under normality.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jonas Andersson & Jarle Møen, 2016. "A Simple Improvement of the IV-estimator for the Classical Errors-in-Variables Problem," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 113-125, February.
    • Handle: RePEc:bla:obuest:v:78:y:2016:i:1:p:113-125
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/obes.12103
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Tor Jakob Klette, 1999. "Market Power, Scale Economies and Productivity: Estimates from a Panel of Establishment Data," Journal of Industrial Economics, Wiley Blackwell, vol. 47(4), pages 451-476, December.
    2. Chao, John C. & Swanson, Norman R. & Hausman, Jerry A. & Newey, Whitney K. & Woutersen, Tiemen, 2012. "Asymptotic Distribution Of Jive In A Heteroskedastic Iv Regression With Many Instruments," Econometric Theory, Cambridge University Press, vol. 28(1), pages 42-86, February.
    3. Mikael Lindahl & Alan B. Krueger, 2001. "Education for Growth: Why and for Whom?," Journal of Economic Literature, American Economic Association, vol. 39(4), pages 1101-1136, December.
    4. Chad Syverson, 2011. "What Determines Productivity?," Journal of Economic Literature, American Economic Association, vol. 49(2), pages 326-365, June.
    5. Jonas Andersson & Jarle Møen, 2016. "A Simple Improvement of the IV-estimator for the Classical Errors-in-Variables Problem," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 113-125, February.
    6. Darren Lubotsky & Martin Wittenberg, 2006. "Interpretation of Regressions with Multiple Proxies," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 549-562, August.
    7. Goldberger, Arthur S, 1972. "Maximum-Likelihood Estimation of Regressions Containing Unobservable Independent Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(1), pages 1-15, February.
    8. George J. Borjas, 2021. "Ethnicity, Neighborhoods, and Human-Capital Externalities," World Scientific Book Chapters, in: Foundational Essays in Immigration Economics, chapter 7, pages 135-160, World Scientific Publishing Co. Pte. Ltd..
    9. Klette, Tor Jakob, 1999. "Market Power, Scale Economies and Productivity: Estimates from a Panel of Establishment Data," Journal of Industrial Economics, Wiley Blackwell, vol. 47(4), pages 451-476, December.
    10. Phillips, Garry D A & Hale, C, 1977. "The Bias of Instrumental Variable Estimators of Simultaneous Equation Systems," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(1), pages 219-228, February.
    11. Behrman, Jere R. & Rosenzweig, Mark R., 1999. ""Ability" biases in schooling returns and twins: a test and new estimates," Economics of Education Review, Elsevier, vol. 18(2), pages 159-167, April.
    12. Rouse, Cecilia Elena, 1999. "Further estimates of the economic return to schooling from a new sample of twins," Economics of Education Review, Elsevier, vol. 18(2), pages 149-157, April.
    13. Ashenfelter, Orley & Krueger, Alan B, 1994. "Estimates of the Economic Returns to Schooling from a New Sample of Twins," American Economic Review, American Economic Association, vol. 84(5), pages 1157-1173, December.
    14. Barron, John M & Berger, Mark C & Black, Dan A, 1997. "How Well Do We Measure Training?," Journal of Labor Economics, University of Chicago Press, vol. 15(3), pages 507-528, July.
    15. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jonas Andersson & Jarle Møen, 2016. "A Simple Improvement of the IV-estimator for the Classical Errors-in-Variables Problem," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 113-125, February.
    2. Erkin Diyarbakirlioglu & Marc Desban & Souad Lajili Jarjir, 2022. "Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms," Post-Print hal-03643083, HAL.
    3. Federico Crudu, 2017. "Errors-in-Variables Models with Many Proxies," Department of Economics University of Siena 774, Department of Economics, University of Siena.

    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. Federico Crudu, 2017. "Errors-in-Variables Models with Many Proxies," Department of Economics University of Siena 774, Department of Economics, University of Siena.
    2. Bronars, Stephen G. & Oettinger, Gerald S., 2006. "Estimates of the return to schooling and ability: evidence from sibling data," Labour Economics, Elsevier, vol. 13(1), pages 19-34, February.
    3. Flora Bellone & Patrick Musso & Lionel Nesta & Frederic Warzynski, 2016. "International trade and firm-level markups when location and quality matter," Journal of Economic Geography, Oxford University Press, vol. 16(1), pages 67-91.
    4. Axel Demenet & Quynh Hoang, 2018. "How important are management practices for the productivity of small and medium enterprises?," WIDER Working Paper Series 69, World Institute for Development Economic Research (UNU-WIDER).
    5. Stephen Martin, 2012. "Market Structure and Market Performance," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 40(2), pages 87-108, March.
    6. Angel de la Fuente & Antonio Ciccone, 2003. "Human capital in a global and knowledge-based economy," UFAE and IAE Working Papers 562.03, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    7. Hawley, Joshua D., 2004. "Changing returns to education in times of prosperity and crisis, Thailand 1985-1998," Economics of Education Review, Elsevier, vol. 23(3), pages 273-286, June.
    8. Jere R. Behrman & Hans-Peter Kohler & Susan Cotts Watkins, 2001. "How can we measure the causal effects of social networks using observational data? Evidence from the diffusion of family planning and AIDS worries in South Nyanza District, Kenya," MPIDR Working Papers WP-2001-022, Max Planck Institute for Demographic Research, Rostock, Germany.
    9. Bound, John & Solon, Gary, 1999. "Double trouble: on the value of twins-based estimation of the return to schooling," Economics of Education Review, Elsevier, vol. 18(2), pages 169-182, April.
    10. Mr. Federico J Diez & Jiayue Fan & Carolina Villegas-Sánchez, 2019. "Global Declining Competition," IMF Working Papers 2019/082, International Monetary Fund.
    11. Krenz, Astrid, 2010. "La distinction reloaded: Returns to education, family background, cultural and social capital in Germany," University of Göttingen Working Papers in Economics 108, University of Goettingen, Department of Economics.
    12. James J. Feigenbaum & Hui Ren Tan, 2019. "The Return to Education in the Mid-20th Century: Evidence from Twins," NBER Working Papers 26407, National Bureau of Economic Research, Inc.
    13. Villegas-Sanchez, Carolina & Díez, Federico & Fan, Jiayue, 2019. "Global Declining Competition," CEPR Discussion Papers 13696, C.E.P.R. Discussion Papers.
    14. Bartelsman, Eric & Dobbelaere, Sabien & Peters, Bettina, 2013. "Allocation of Human Capital and Innovation at the Frontier: Firm-Level Evidence on Germany and the Netherlands," IZA Discussion Papers 7540, Institute of Labor Economics (IZA).
    15. Barge-Gil, Andrés & López, Alberto, 2013. "R&D and productivity: In search of complementarity between research and development activities," MPRA Paper 43808, University Library of Munich, Germany.
    16. Reuben Gronau, 2010. "Zvi Griliches' Contribution to the Theory of Human Capital," NBER Chapters, in: Contributions in Memory of Zvi Griliches, pages 275-297, National Bureau of Economic Research, Inc.
    17. Zhang, Junsen & Liu, Pak-Wai & Yung, Linda, 2007. "The Cultural Revolution and returns to schooling in China: Estimates based on twins," Journal of Development Economics, Elsevier, vol. 84(2), pages 631-639, November.
    18. George Psacharopoulos & Harry Anthony Patrinos, 2004. "Returns to investment in education: a further update," Education Economics, Taylor & Francis Journals, vol. 12(2), pages 111-134.
    19. repec:got:cegedp:108 is not listed on IDEAS
    20. Richard Fabling & David C Maré, 2015. "Production function estimation using New Zealand’s Longitudinal Business Database," Working Papers 15_15, Motu Economic and Public Policy Research.
    21. Tracy L. Regan & Ronald L. Oaxaca & Galen Burghardt, 2007. "A Human Capital Model Of The Effects Of Ability And Family Background On Optimal Schooling Levels," Economic Inquiry, Western Economic Association International, vol. 45(4), pages 721-738, October.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - 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:bla:obuest:v:78:y:2016:i:1:p:113-125. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/sfeixuk.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.