IDEAS home Printed from https://ideas.repec.org/p/isu/genstf/1997010108000013019.html
   My bibliography  Save this paper

Econometric analysis of measurement error in panel data

Author

Listed:
  • Paterno, Elizabeth Martha

Abstract

Panel data consist of measurements taken from several individuals over time. Correlation among measurements taken from the same individual are often accounted for using random effect and random coefficient models. Panel data analysis that accounts for measurement error in the explanatory variables has not been thoroughly studied. This dissertation investigates statistical issues associated with two types of measurement error models for panel data;The first paper considers identification and estimation of a random effect model when some explanatory variables are measured with error. Here, individual heterogeneity is assumed to be manifested in intercepts that randomly differ across individuals. Identification of model parameters given the first two moments of observed variables is examined, and relatively unrestrictive sufficient conditions for identification are obtained. Estimation based on maximum normal likelihood is proposed. This method can be easily implemented using available computer packages that perform moment structure analysis. Compared to the only existing procedure based on instrumental variables, the new method is shown to be more efficient and to have much wider applicability. Standard error estimates and goodness-of-fit statistics obtained under the assumption of normally distributed observations are shown to be asymptotically valid for a broad class of non-normal observations. Simulation results demonstrating the efficiency and usefulness of the new procedure are presented;The second paper deals with the random coefficient model with measurement error, where all regression coefficients randomly differ across individuals. Two procedures are proposed for model fitting and estimation. The generalized least squares method is developed for the first two sample moments with a distribution-free estimate of the weight. Since this method tends to yield very variable estimates in small samples, an alternative method, the pseudo maximum normal likelihood procedure is also developed. The latter, obtained by maximizing a hypothetical normal likelihood for the first two sample moments, produces relative stable estimates in most samples. Asymptotic properties of the two procedures are derived and are used to obtain valid standard errors of the estimators. Numerical results showing the finite-sample properties of these estimators are also reported.

Suggested Citation

  • Paterno, Elizabeth Martha, 1997. "Econometric analysis of measurement error in panel data," ISU General Staff Papers 1997010108000013019, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genstf:1997010108000013019
    as

    Download full text from publisher

    File URL: https://dr.lib.iastate.edu/server/api/core/bitstreams/cee31a86-fdd0-423f-a020-2a4db645a2e6/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    2. T.J. Wansbeek & R.H. Koning, 1991. "Measurement error and panel data1," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 45(2), pages 85-92, 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. 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.
    2. Gabriel Montes-Rojas, 2011. "Quantile Regression with Classical Additive Measurement Errors," Economics Bulletin, AccessEcon, vol. 31(4), pages 2863-2868.
    3. Biørn, Erik, 2012. "The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation," Memorandum 02/2012, Oslo University, Department of Economics.
    4. 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.
    5. 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.
    6. Biørn, Erik & Han, Xuehui, 2012. "Panel Data Dynamics and Measurement Errors: GMM Bias, IV Validity and Model Fit – A Monte Carlo Study," Memorandum 27/2012, Oslo University, Department of Economics.
    7. Ramses ABUL NAGA & Jaya KRISHNAKUMAR, 1999. "Panel Data Estimation of the Intergenerational Correlation of Incomes," Cahiers de Recherches Economiques du Département d'économie 9910, Université de Lausanne, Faculté des HEC, Département d’économie.
    8. Galvao Jr, A. F. & Montes-Rojas, G., 2009. "Instrumental variables quantile regression for panel data with measurement errors," Working Papers 09/06, Department of Economics, City University London.
    9. 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.
    10. Tybout, James R. & Westbrook, M. Daniel, 1995. "Trade liberalization and the dimensions of efficiency change in Mexican manufacturing industries," Journal of International Economics, Elsevier, vol. 39(1-2), pages 53-78, August.
    11. Daron Acemoglu & Amy Finkelstein, 2008. "Input and Technology Choices in Regulated Industries: Evidence from the Health Care Sector," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 837-880, October.
    12. Imam, M. & Jamasb, T. & Llorca, M. & Llorca, M., 2018. "Power Sector Reform and Corruption: Evidence from Electricity Industry in Sub-Saharan Africa," Cambridge Working Papers in Economics 1801, Faculty of Economics, University of Cambridge.
    13. Castelló-Climent, Amparo & Mukhopadhyay, Abhiroop, 2013. "Mass education or a minority well educated elite in the process of growth: The case of India," Journal of Development Economics, Elsevier, vol. 105(C), pages 303-320.
    14. Burks, Stephen V & Guy, Frederick & Maxwell, Benjamin, 2004. "7. Shifting Gears In The Corner Office: Deregulation And The Earnings Of Trucking Executives," Research in Transportation Economics, Elsevier, vol. 10(1), pages 137-164, January.
    15. Prasanna Tambe & Lorin M. Hitt, 2014. "Measuring Information Technology Spillovers," Information Systems Research, INFORMS, vol. 25(1), pages 53-71, March.
    16. P. Charnoz & C. Lelarge & C. Trevien, 2016. "Communication Costs and the Internal Organization of Multi-Plant Businesses: Evidence from the Impact of the French High-Speed Rail," Documents de Travail de l'Insee - INSEE Working Papers g2016-02, Institut National de la Statistique et des Etudes Economiques.
    17. Lin, Dajun & Lutter, Randall & Ruhm, Christopher J., 2018. "Cognitive performance and labour market outcomes," Labour Economics, Elsevier, vol. 51(C), pages 121-135.
    18. Tadadjeu, Sosson & Njangang, Henri & Asongu, Simplice A. & Kamguia, Brice, 2023. "Natural resources, child mortality and governance quality in African countries," Resources Policy, Elsevier, vol. 83(C).
    19. Zhou, Xianming, 2001. "Understanding the determinants of managerial ownership and the link between ownership and performance: comment," Journal of Financial Economics, Elsevier, vol. 62(3), pages 559-571, December.
    20. Martin Biewen & Constantin Weiser, 2014. "An empirical test of marginal productivity theory," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 996-1020, March.

    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:isu:genstf:1997010108000013019. 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: Curtis Balmer (email available below). General contact details of provider: https://edirc.repec.org/data/deiasus.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.