IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v55y2020i2d10.1007_s10614-019-09907-w.html
   My bibliography  Save this article

Quantifying the Advantages of Forward Orthogonal Deviations for Long Time Series

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-019-09907-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10614-019-09907-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. 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.
    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. 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, 2019. "A Comparison of First-Difference and Forward Orthogonal Deviations GMM," Papers 1907.12880, arXiv.org.
    4. Robert F. Phillips, 2022. "Forward Orthogonal Deviations GMM and the Absence of Large Sample Bias," Papers 2212.14075, arXiv.org.

    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. Badi H. Baltagi, 2021. "Dynamic Panel Data Models," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 187-228, Springer.
    2. Okui, Ryo, 2009. "The optimal choice of moments in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 151(1), pages 1-16, July.
    3. Hayakawa, Kazuhiko, 2019. "Alternative over-identifying restriction test in the GMM estimation of panel data models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 71-95.
    4. Han, Chirok & Phillips, Peter C. B. & Sul, Donggyu, 2014. "X-Differencing And Dynamic Panel Model Estimation," Econometric Theory, Cambridge University Press, vol. 30(1), pages 201-251, February.
    5. Robert F. Phillips, 2022. "Forward Orthogonal Deviations GMM and the Absence of Large Sample Bias," Papers 2212.14075, arXiv.org.
    6. Hujer Reinhard & Rodrigues Paulo J. M. & Wolf Katja, 2008. "Dynamic Panel Data Models with Spatial Correlation," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(5-6), pages 612-629, October.
    7. Robert F. Phillips, 2019. "A Comparison of First-Difference and Forward Orthogonal Deviations GMM," Papers 1907.12880, arXiv.org.
    8. De Blander, Rembert, 2020. "Iterative estimation correcting for error auto-correlation in short panels, applied to lagged dependent variable models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 3-29.
    9. Phillips, Robert F., 2019. "A numerical equivalence result for generalized method of moments," Economics Letters, Elsevier, vol. 179(C), pages 13-15.
    10. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    11. Bun, Maurice J.G. & Kiviet, Jan F., 2006. "The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 409-444, June.
    12. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    13. 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.
    14. Ferdi Celikay, 2020. "Dimensions of tax burden: a review on OECD countries," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, vol. 25(49), pages 27-43, March.
    15. Kin Sibanda & Rufaro Garidzirai & Farai Mushonga & Dorcas Gonese, 2023. "Natural Resource Rents, Institutional Quality, and Environmental Degradation in Resource-Rich Sub-Saharan African Countries," Sustainability, MDPI, vol. 15(2), pages 1-11, January.
    16. Antonio Ruiz Porras, 2016. "La investigación econométrica mediante paneles de datos:historia, modelos y usos en México," Archivos Revista Economía y Política., Facultad de Ciencias Económicas y Administrativas, Universidad de Cuenca., vol. 24, pages 11-32, Julio.
    17. Hall, B. & Mairesse, J. & Branstetter, L. & Crepon, B., 1998. "Does Cash Flow cause Investment and R&D: An Exploration Using Panel Data for French, Japanese, and United States Scientific Firms," Economics Papers 142, Economics Group, Nuffield College, University of Oxford.
    18. Baum, Anja & Checherita-Westphal, Cristina & Rother, Philipp, 2013. "Debt and growth: New evidence for the euro area," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 809-821.
    19. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Forecasting With Dynamic Panel Data Models," Econometrica, Econometric Society, vol. 88(1), pages 171-201, January.
    20. Mayer, Alexander, 2022. "On the local power of some tests of strict exogeneity in linear fixed effects models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 49-74.

    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:kap:compec:v:55:y:2020:i:2:d:10.1007_s10614-019-09907-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.