IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0037.html
   My bibliography  Save this paper

Errors in Variables in Panel Data

Author

Listed:
  • Zvi Griliches
  • Jerry A. Hausman

Abstract

Panel data based on various longitudinal surveys have become ubiquitous in economics in recent years. Estimation using the analysis of covariance approach allows for control of various "individual effects" by estimation of the relevant relationships from the "within" dimension of the data. Quite often, however, the "within" results are unsatisfactory, "too low" and insignificant. Errors of measurement in the independent variables whose relative importance gets magnified in the within dimension are often blamed for this outcome. However, the standard errors-in-variables model has not been applied widely, partly because in the usual micro data context it requires extraneous information to identify the parameters of interest. In the panel data context a variety of errors-in-variables models may be identifiable and estimable without the use of external instruments. We develop this idea and illustrate its application in a relatively simple but not uninteresting case: the estimation of "labor demand" relationships, also known as the "short run increasing returns to scale" puzzle.

Suggested Citation

  • Zvi Griliches & Jerry A. Hausman, 1984. "Errors in Variables in Panel Data," NBER Technical Working Papers 0037, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0037
    Note: LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0037.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Christopher A. Sims, 1974. "Output and Labor Input in Manufacturing," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 5(3), pages 695-736.
    3. Bronwyn H. Hall & Clint Cumminq & Elizabeth S. Laderman & Joy Mundy, 1988. "The R&D Master File Documentation," NBER Technical Working Papers 0072, National Bureau of Economic Research, Inc.
    4. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    5. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    6. Griliches, Zvi, 1979. "Sibling Models and Data in Economics: Beginnings of a Survey," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 37-64, October.
    7. White, Halbert, 1982. "Instrumental Variables Regression with Independent Observations," Econometrica, Econometric Society, vol. 50(2), pages 483-499, March.
    8. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    9. Maddala, G S, 1971. "The Use of Variance Components Models in Pooling Cross Section and Time Series Data," Econometrica, Econometric Society, vol. 39(2), pages 341-358, March.
    10. F. P. R. Brechling, 1965. "The Relationship between Output and Employment in British Manufacturing Industries," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 32(3), pages 187-216.
    11. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    12. Milton Friedman, 1957. "A Theory of the Consumption Function," NBER Books, National Bureau of Economic Research, Inc, number frie57-1, June.
    13. Chamberlain, Gary & Griliches, Zvi, 1975. "Unobservables with a Variance-Components Structure: Ability, Schooling, and the Economic Success of Brothers," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(2), pages 422-449, 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. Ranjita Pandey & Anoop Chaturvedi, 2016. "Bayesian Inference For State Space Model With Panel Data," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 211-219, June.
    2. Theodore Joyce & Andrew D. Racine & Naci Mocan, 1992. "The Consequences and Costs of Maternal Substance Abuse in New York City," NBER Working Papers 3987, National Bureau of Economic Research, Inc.
    3. 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.
    4. John Geweke & Joel Horowitz & M. Hashem Pesaran, 2006. "Econometrics: A Bird’s Eye View," CESifo Working Paper Series 1870, CESifo.
    5. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    6. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    7. Nikolaev, V. & van Lent, L.A.G.M., 2005. "The Endogeneity Bias in the Relation Between Cost-of-Debt Capital and Corporate Disclosure Policy," Discussion Paper 2005-67, Tilburg University, Center for Economic Research.
    8. Manuel Arellano & Olympia Bover, 1990. "La econometría de datos de panel," Investigaciones Economicas, Fundación SEPI, vol. 14(1), pages 3-45, January.
    9. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    10. Ahn, Seung C. & Low, Stuart, 1996. "A reformulation of the Hausman test for regression models with pooled cross-section-time-series data," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 309-319.
    11. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.
    12. Jean-Bernard Chatelain & Kirsten Ralf, 2010. "Inference on Time-Invariant Variables using Panel Data: A Pre-Test Estimator with an Application to the Returns to Schooling," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00492039, HAL.
    13. Nikolaev, V. & van Lent, L.A.G.M., 2005. "The Endogeneity Bias in the Relation Between Cost-of-Debt Capital and Corporate Disclosure Policy," Other publications TiSEM 5960a342-0adc-4f85-bf87-2, Tilburg University, School of Economics and Management.
    14. Nikolaev, V. & van Lent, L.A.G.M., 2005. "The endogeneity bias in the relation between cost-of-debt capital and corporate disclosure policy," Other publications TiSEM 04869b30-e8a9-4ecf-84ae-6, Tilburg University, School of Economics and Management.
    15. Joshua D. Angrist & Jinyong Hahn, 1999. "When to Control for Covariates? Panel-Asymptotic Results for Estimates of Treatment Effects," NBER Technical Working Papers 0241, National Bureau of Economic Research, Inc.
    16. Silvio R. Rendon, 2013. "Fixed and Random Effects in Classical and Bayesian Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 460-476, June.
    17. James P. Ziliak, 2003. "Income Transfers and Assets of the Poor," The Review of Economics and Statistics, MIT Press, vol. 85(1), pages 63-76, February.
    18. Sebastian Kripfganz, 2017. "Sequential (two-stage) estimation of linear panel-data models," German Stata Users' Group Meetings 2017 03, Stata Users Group.
    19. Arnd Kölling & Claus Schnabel, 2022. "Owners, external managers and industrial relations in German establishments," British Journal of Industrial Relations, London School of Economics, vol. 60(2), pages 424-443, June.
    20. Jacques Mairesse & Bronwyn H. Hall & Benoît Mulkay, 1999. "Firm-Level Investment in France and the United States: An Exploration of What We Have Learned in Twenty Years," Annals of Economics and Statistics, GENES, issue 55-56, pages 27-67.

    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:nbr:nberte:0037. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.