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Quantifying the Advantages of Forward Orthogonal Deviations for Long Time Series

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  • Robert F. Phillips

    (George Washington University)

Abstract

Under suitable conditions, generalized method of moments (GMM) estimates can be computed using a comparatively fast computational technique: filtered two-stage least squares (2SLS). This fact is illustrated with a special case of filtered 2SLS—specifically, the forward orthogonal deviations (FOD) transformation. If a restriction on the instruments is satisfied, GMM based on the FOD transformation (FOD-GMM) is identical to GMM based on the more popular first-difference (FD) transformation (FD-GMM). However, the FOD transformation provides significant reductions in computing time when the length of the time series (T) is not small. If the instruments condition is not met, the FD and FOD transformations lead to different GMM estimators. In this case, the computational advantage of the FOD transformation over the FD transformation is not as dramatic. On the other hand, in this case, Monte Carlo evidence provided in the paper indicates that FOD-GMM has better sampling properties—smaller absolute bias and standard deviations. Moreover, if T is not small, the FOD-GMM estimator has better sampling properties than the FD-GMM estimator even when the latter estimator is based on the optimal weighting matrix. Hence, when T is not small, FOD-GMM dominates FD-GMM in terms of both computational efficiency and sampling performance.

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  • Robert F. Phillips, 2020. "Quantifying the Advantages of Forward Orthogonal Deviations for Long Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 653-672, February.
    • Handle: RePEc:kap:compec:v:55:y:2020:i:2:d:10.1007_s10614-019-09907-w
    DOI: 10.1007/s10614-019-09907-w
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    References listed on IDEAS

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    1. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    2. Hayakawa, Kazuhiko & Nagata, Shuichi, 2016. "On the behaviour of the GMM estimator in persistent dynamic panel data models with unrestricted initial conditions," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 265-303.
    3. Phillips, Robert F., 2019. "A numerical equivalence result for generalized method of moments," Economics Letters, Elsevier, vol. 179(C), pages 13-15.
    4. Keane, Michael P & Runkle, David E, 1992. "On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 1-9, January.
    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. Kazuhiko Hayakawa, 2009. "First Difference or Forward Orthogonal Deviation- Which Transformation Should be Used in Dynamic Panel Data Models?: A Simulation Study," Economics Bulletin, AccessEcon, vol. 29(3), pages 2008-2017.
    7. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    8. Keane, Michael P & Runkle, David E, 1992. "On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 26-29, January.
    9. 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.
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    Cited by:

    1. Michael Landesmann & Isilda Mara, 2021. "Interrelationships between Human Capital, Migration and Labour Markets in the Western Balkans: An Econometric Investigation," wiiw Working Papers 196, The Vienna Institute for International Economic Studies, wiiw.
    2. Robert F. Phillips, 2020. "The equivalence of two-step first difference and forward orthogonal deviations GMM," Economics Bulletin, AccessEcon, vol. 40(4), pages 2865-2871.
    3. Robert F. Phillips, 2022. "Forward Orthogonal Deviations GMM and the Absence of Large Sample Bias," Papers 2212.14075, arXiv.org, revised Jul 2024.
    4. Robert F. Phillips, 2019. "A Comparison of First-Difference and Forward Orthogonal Deviations GMM," Papers 1907.12880, arXiv.org.

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