IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v59y1998i3p275-282.html
   My bibliography  Save this article

Panel data with errors-in-variables: essential and redundant orthogonality conditions in GMM-estimation

Author

Listed:
  • Biorn, Erik
  • Klette, Tor Jakob

Abstract

No abstract is available for this item.

Suggested Citation

  • Biorn, Erik & Klette, Tor Jakob, 1998. "Panel data with errors-in-variables: essential and redundant orthogonality conditions in GMM-estimation," Economics Letters, Elsevier, vol. 59(3), pages 275-282, June.
    • Handle: RePEc:eee:ecolet:v:59:y:1998:i:3:p:275-282
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(98)00053-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    References listed on IDEAS

    as
    1. James H. Stock & Jonathan Wright, 1996. "Asymptotics for GMM Estimators with Weak Instruments," NBER Technical Working Papers 0198, National Bureau of Economic Research, Inc.
    2. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    3. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    4. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, Decembrie.
    5. Biorn, E. & Klette, T.J., 1997. "Variable Differencing and GMM Estimation with Panel Data with Errors-In-Variables," Memorandum 1997_016, Oslo University, Department of Economics.
    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. Pierani, P.; Tiezzi, S.;, 2017. "Rational addiction and time consistency:an empirical test," Health, Econometrics and Data Group (HEDG) Working Papers 17/05, HEDG, c/o Department of Economics, University of York.
    2. Piccoli, Luca & Tiezzi, Silvia, 2021. "Rational addiction and time-consistency: An empirical test," Journal of Health Economics, Elsevier, vol. 80(C).
    3. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    4. 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.
    5. 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.
    6. Andrew M. Jones & José M. Labeaga, 2003. "Individual heterogeneity and censoring in panel data estimates of tobacco expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 157-177.
    7. Amir Khordehfrosh Dilmaghani & Amir Mansour Tehranchian, 2015. "The Impact of Monetary Policies on the Exchange Rate: A GMM Approach," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 19(2), pages 177-191, Spring.
    8. Wenqin Pan & Donglin Zeng & Xihong Lin, 2009. "Estimation in Semiparametric Transition Measurement Error Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 65(3), pages 728-736, September.
    9. Elena Biewen & Gerd Ronning & Martin Rosemann, 2009. "IV-Schätzung eines linearen Panelmodells mit stochastisch überlagerten Betriebs- und Unternehmensdaten," IAW Discussion Papers 53, Institut für Angewandte Wirtschaftsforschung (IAW).

    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. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    2. 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.
    3. Mario Jametti & Thomas von Ungern-Sternberg, 2005. "Assessing the Efficiency of an Insurance Provider—A Measurement Error Approach," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 30(1), pages 15-34, June.
    4. Måns Söderbom & Francis Teal, 2003. "Openness and human capital as sources of productivity growth: An empirical investigation," CSAE Working Paper Series 2003-06, Centre for the Study of African Economies, University of Oxford.
    5. Sabine Herrmann & Joern Kleinert, 2014. "Lucas Paradox and Allocation Puzzle - Is the euro area different?," Graz Economics Papers 2014-01, University of Graz, Department of Economics.
    6. O'Brien, Raymond & Patacchini, Eleonora, 2003. "Testing the exogeneity assumption in panel data models with "non classical" disturbances," Discussion Paper Series In Economics And Econometrics 0302, Economics Division, School of Social Sciences, University of Southampton.
    7. Doko Tchatoka, Firmin & Dufour, Jean-Marie, 2020. "Exogeneity tests, incomplete models, weak identification and non-Gaussian distributions: Invariance and finite-sample distributional theory," Journal of Econometrics, Elsevier, vol. 218(2), pages 390-418.
    8. Jack Hadley & Daniel Polsky & Jeanne S. Mandelblatt & Jean M. Mitchell & Jane C. Weeks & Qin Wang & Yi‐Ting Hwang & OPTIONS Research Team, 2003. "An exploratory instrumental variable analysis of the outcomes of localized breast cancer treatments in a medicare population," Health Economics, John Wiley & Sons, Ltd., vol. 12(3), pages 171-186, March.
    9. Frank Windmeijer & Helmut Farbmacher & Neil Davies & George Davey Smith, 2019. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1339-1350, July.
    10. Yong Cai, 2022. "Linear Regression with Centrality Measures," Papers 2210.10024, arXiv.org.
    11. William Hauk & Romain Wacziarg, 2009. "A Monte Carlo study of growth regressions," Journal of Economic Growth, Springer, vol. 14(2), pages 103-147, June.
    12. Poncet, Sandra & Steingress, Walter & Vandenbussche, Hylke, 2010. "Financial constraints in China: Firm-level evidence," China Economic Review, Elsevier, vol. 21(3), pages 411-422, September.
    13. Rodrigo Alfaro, 2008. "Higher Order Properties of the Symmetricallr Normalized Instrumental Variable Estimator," Working Papers Central Bank of Chile 500, Central Bank of Chile.
    14. Brown, Keith & Alexander, Peter J., 2005. "Market structure, viewer welfare, and advertising rates in local broadcast television markets," Economics Letters, Elsevier, vol. 86(3), pages 331-337, March.
    15. Tamara Fioroni & Andrea Mario Lavezzi & Giovanni Trovato, 2023. "Organized Crime, Corruption and Economic Growth," Discussion Papers 2023/298, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    16. Yuriy Pylypchuk & Samuel W. Norton, 2015. "Preventing Malaria among Children in Zambia: The Role of Mother's Knowledge," Health Economics, John Wiley & Sons, Ltd., vol. 24(11), pages 1389-1402, November.
    17. Maral Kichian & Linda Khalaf, 2000. "Testing The Pricing-To-Market Hypothesis Case Of The Transportation Equipment Industry," Computing in Economics and Finance 2000 58, Society for Computational Economics.
    18. Souleymane COULIBALY, 2008. "Empirical Assessment of the Existence of Taxable Agglomeration Rents," Cahiers de Recherches Economiques du Département d'économie 08.01, Université de Lausanne, Faculté des HEC, Département d’économie.
    19. Leith, Campbell & Malley, Jim, 2005. "Estimated general equilibrium models for the evaluation of monetary policy in the US and Europe," European Economic Review, Elsevier, vol. 49(8), pages 2137-2159, November.
    20. Michal Kolesár & Raj Chetty & John Friedman & Edward Glaeser & Guido W. Imbens, 2015. "Identification and Inference With Many Invalid Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 474-484, October.

    More about this item

    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:eee:ecolet:v:59:y:1998:i:3:p:275-282. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.