IDEAS home Printed from https://ideas.repec.org/p/hhs/osloec/2015_004.html
   My bibliography  Save this paper

Persistence, Signal-Noise Pattern and Heterogeneity in Panel Data: With an Application to the Impact of Foreign Direct Investment on GDP

Author

Listed:
  • Biørn, Erik

    (Dept. of Economics, University of Oslo)

  • Han, Xuehui

    (Economics and Research Department)

Abstract

GMM estimation of autoregressive panel data equations in error-ridden variables when the noise has memory, is considered. The impact of variation in the memory length in signal and noise spread and in the degree of individual heterogeneity are discussed with respect to finite sample bias, using Monte Carlo simulations. Also explored are also the impact of the strength of autocorrelation and the size of the IV set. GMM procedures using IVs in differences on equations in levels, in general perform better in small samples than procedures using IVs in levels on equations in differences. A case-study of the impact of Foreign Direct Investment (FDI) on GDP, inter alia, contrasting the manufacturing and the service sector, based on country panel data supplements the simulation results.

Suggested Citation

  • Biørn, Erik & Han, Xuehui, 2015. "Persistence, Signal-Noise Pattern and Heterogeneity in Panel Data: With an Application to the Impact of Foreign Direct Investment on GDP," Memorandum 04/2015, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2015_004
    as

    Download full text from publisher

    File URL: http://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2015/memo-04-2015.pdf
    Download Restriction: no
    ---><---

    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. 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.
    4. Wansbeek, Tom, 2001. "GMM estimation in panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 104(2), pages 259-268, September.
    5. 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.
    6. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    7. 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.
    8. 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.
    9. Basu, Parantap & Guariglia, Alessandra, 2007. "Foreign Direct Investment, inequality, and growth," Journal of Macroeconomics, Elsevier, vol. 29(4), pages 824-839, December.
    10. Marco Neuhaus, 2006. "The Impact of FDI on Economic Growth," Contributions to Economics, Springer, number 978-3-7908-1735-5.
    11. 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.
    12. 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.
    13. 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.
    14. Doytch, Nadia & Uctum, Merih, 2011. "Does the worldwide shift of FDI from manufacturing to services accelerate economic growth? A GMM estimation study," Journal of International Money and Finance, Elsevier, vol. 30(3), pages 410-427, April.
    15. 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)

    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 & Xuehui Han, 2017. "Revisiting the FDI impact on GDP growth in errors-in-variables models: a panel data GMM analysis allowing for error memory," Empirical Economics, Springer, vol. 53(4), pages 1379-1398, December.
    2. 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.
    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. Erik Biørn, 2015. "Panel data dynamics with mis-measured variables: modeling and GMM estimation," Empirical Economics, Springer, vol. 48(2), pages 517-535, March.
    5. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    6. Yannis Psycharis & Stavroula Iliopoulou & Maria Zoi & Panagiotis Pantazis, 2021. "Beyond the socio‐economic use of fiscal transfers: The role of political factors in Greek intergovernmental grant allocations," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 982-1008, June.
    7. 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.
    8. Robertson, Donald & Sarafidis, Vasilis, 2015. "IV estimation of panels with factor residuals," Journal of Econometrics, Elsevier, vol. 185(2), pages 526-541.
    9. 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.
    10. Ilse Ruyssen & Gerdie Everaert & Glenn Rayp, 2014. "Determinants and dynamics of migration to OECD countries in a three-dimensional panel framework," Empirical Economics, Springer, vol. 46(1), pages 175-197, February.
    11. Emilia Bonaccorsi di Patti & Francesco Palazzo, 2020. "Bank profitability and macroeconomic conditions: Are business models different?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 49(2), July.
    12. Assi Okara, 2022. "Building Stronger Economic Institutions in Developing Countries, the Role of FDI," CERDI Working papers hal-03617915, HAL.
    13. Ignace De Vos & Gerdie Everaert & Ilse Ruyssen, 2015. "Bootstrap-based bias correction and inference for dynamic panels with fixed effects," Stata Journal, StataCorp LP, vol. 15(4), pages 986-1018, December.
    14. Maurice J. G. Bun & Richard Kelaher & Vasilis Sarafidis & Don Weatherburn, 2020. "Crime, deterrence and punishment revisited," Empirical Economics, Springer, vol. 59(5), pages 2303-2333, November.
    15. Assi Okara, 2022. "Building Stronger Economic Institutions in Developing Countries, the Role of FDI," Working Papers hal-03617915, HAL.
    16. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    17. Opolska, Iweta, 2017. "The efficacy of liberalization and privatization in introducing competition into European natural gas markets," Utilities Policy, Elsevier, vol. 48(C), pages 12-21.
    18. Norman Loayza & Klaus Schmidt-Hebbel & Luis Servén, 2000. "What Drives Private Saving Across the World?," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 165-181, May.
    19. Okui, Ryo, 2009. "The optimal choice of moments in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 151(1), pages 1-16, July.
    20. Jan F. Kiviet & Milan Pleus & Rutger Poldermans, 2014. "Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models," UvA-Econometrics Working Papers 14-09, Universiteit van Amsterdam, Dept. of Econometrics.

    More about this item

    Keywords

    Panel data; Measurement error; ARMA; GMM; Error memory; Monte Carlo; Foreign Direct Investment; Economic development; Country panel;
    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
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:osloec:2015_004. 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: Mari Strønstad Øverås (email available below). General contact details of provider: https://edirc.repec.org/data/souiono.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.