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The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation

Author

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  • Biørn, Erik

    (Dept. of Economics, University of Oslo)

Abstract

The Generalized Method of Moments (GMM) is discussed for handling the joint occurrence of fixed effects and random measurement errors in an autoregressive panel data model. Finite memory of disturbances, latent regressors and measurement errors is assumed. Two specializations of GMM are considered: (i) using instruments (IVs) in levels for a differenced version of the equation, (ii) using IVs in differences for an equation in levels. Index sets for lags and lags are convenient in examining how the potential IV set, satisfying orthogonality and rank conditions, changes when the memory pattern changes. The joint occurrence of measurement errors with long memory may sometimes give an IV-set too small to make estimation possible. On the other hand, problems of ‘IV proliferation’ and ‘weak IVs’ may arise unless the time-series length is small. An application based on data for (log-transformed) capital stock and output from Norwegian manufacturing firms is discussed. Finite sample biases and IV quality are illustrated by Monte Carlo simulations. Overall, with respect to bias and IV strength, GMM inference using the level version of the equation seems superior to inference based on the equation in differences.

Suggested Citation

  • Biørn, Erik, 2012. "The Measurement Error Problem in Dynamic Panel Data Analysis: Modeling and GMM Estimation," Memorandum 02/2012, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2012_002
    as

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    File URL: https://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2012/Memo-02-2012.pdf
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    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. Pesaran, M Hashem & Smith, Richard J, 1994. "A Generalized R[superscript]2 Criterion for Regression Models Estimated by the Instrumental Variables Method," Econometrica, Econometric Society, vol. 62(3), pages 705-710, May.
    3. Nowak, Eugen, 1993. "The identification of multivariate linear dynamic errors-in-variables models," Journal of Econometrics, Elsevier, vol. 59(3), pages 213-227, October.
    4. Mark Harris & Laszlo Matyas & Patrick Sevestre, 2008. "Dynamic Models for Short Panels," Post-Print halshs-00279980, HAL.
    5. 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.
    6. Wansbeek, Tom, 2001. "GMM estimation in panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 104(2), pages 259-268, September.
    7. Biorn, Erik, 1992. "The Bias of Some Estimators for Panel Data Models with Measurement Errors," Empirical Economics, Springer, vol. 17(1), pages 51-66.
    8. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    9. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    10. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    11. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    12. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    13. K. Newey, Whitney, 1985. "Generalized method of moments specification testing," Journal of Econometrics, Elsevier, vol. 29(3), pages 229-256, September.
    14. Staudenmayer, John & Buonaccorsi, John P., 2005. "Measurement Error in Linear Autoregressive Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 841-852, September.
    15. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    16. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    17. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    18. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    19. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    20. Ziliak, James P, 1997. "Efficient Estimation with Panel Data When Instruments Are Predetermined: An Empirical Comparison of Moment-Condition Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 419-431, October.
    21. Grether, D M & Maddala, G S, 1973. "Errors in Variables and Serially Correlated Disturbances in Distributed Lag Models," Econometrica, Econometric Society, vol. 41(2), pages 255-262, March.
    22. Patrick Sevestre & Laszlo Matyas, 2008. "The Econometrics of Panel Data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279977, HAL.
    23. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    24. 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.
    25. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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    More about this item

    Keywords

    Panel data; Measurement error; Dynamic modeling; ARMA model; GMM; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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