IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/70627.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this paper

R-Codes to Calculate GMM Estimations for Dynamic Panel Data Models

Author

Listed:
  • Abonazel, Mohamed R.

Abstract

These codes presented three functions for calculating three important estimators in dynamic panel data (DPD) models; these estimators are Arellano-Bond (1991), Arellano-Bover (1995), and Blundell-Bond (1998). All functions here need to the following variables: yit_1: dependent variable for DPD model; phi: the value of autoregressive coefficient; D.T_D.T: first-difference operator matrix of Arellano-Bond estimator; HD: instrumental variables of Arellano-Bond estimator; HL: instrumental variables of Arellano-Bover estimator; W: weighting matrix of Blundell-Bond estimator; HS: instrumental variables of Blundell-Bond estimator. Also, they need to the following R libraries: simex; plm; dlm. For more details about the theoretical bases and the developments of that estimators, see, e.g., Youssef et al. (2014a,b) and Youssef and Abonazel (2015). Moreover, these codes have been designed to enable the user to make a simulation study in this topic, such as the simulation study in Youssef et al. (2014b).

Suggested Citation

  • Abonazel, Mohamed R., 2015. "R-Codes to Calculate GMM Estimations for Dynamic Panel Data Models," MPRA Paper 70627, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:70627
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/70627/1/MPRA_paper_70627.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Youssef, Ahmed H. & El-Sheikh, Ahmed A. & Abonazel, Mohamed R., 2014. "Improving the Efficiency of GMM Estimators for Dynamic Panel Models," MPRA Paper 68675, University Library of Munich, Germany.
    2. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    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. Youssef, Ahmed H. & El-Sheikh, Ahmed A. & Abonazel, Mohamed R., 2014. "New GMM Estimators for Dynamic Panel Data Models," MPRA Paper 68676, University Library of Munich, Germany.
    5. 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.
    6. 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. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.

    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. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
    2. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    3. Charalampos Agiropoulos & Michael L. Polemis & Michael Siopsis & Sotiris Karkalakos, 2022. "Revisiting the finance‐growth nexus: A socioeconomic approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2762-2783, July.
    4. Youssef, Ahmed H. & El-Sheikh, Ahmed A. & Abonazel, Mohamed R., 2014. "New GMM Estimators for Dynamic Panel Data Models," MPRA Paper 68676, University Library of Munich, Germany.
    5. Daniel Ştefan Armeanu & Georgeta Vintilă & Ştefan Cristian Gherghina, 2017. "Empirical Study towards the Drivers of Sustainable Economic Growth in EU-28 Countries," Sustainability, MDPI, vol. 10(1), pages 1-22, December.
    6. Youngho Kang & Byung-Yeon Kim, 2018. "Immigration and economic growth: do origin and destination matter?," Applied Economics, Taylor & Francis Journals, vol. 50(46), pages 4968-4984, October.
    7. Vieira, Flávio & MacDonald, Ronald & Damasceno, Aderbal, 2012. "The role of institutions in cross-section income and panel data growth models: A deeper investigation on the weakness and proliferation of instruments," Journal of Comparative Economics, Elsevier, vol. 40(1), pages 127-140.
    8. Kitazawa, Yoshitsugu, 2001. "Exponential regression of dynamic panel data models," Economics Letters, Elsevier, vol. 73(1), pages 7-13, October.
    9. Alessandra Canepa & Fawaz Khaled, 2018. "Housing, Housing Finance and Credit Risk," IJFS, MDPI, vol. 6(2), pages 1-23, May.
    10. Efobi, Uchenna & Asongu, Simplice & Okafor, Chinelo & Tchamyou, Vanessa & Tanankem, Belmondo, 2016. "Diaspora Remittance Inflow, Financial Development and the Industrialisation of Africa," MPRA Paper 76121, University Library of Munich, Germany.
    11. Eschenhof, Sabine, 2009. "Standard Taylor rules revisited: A cross country study for European countries," Darmstadt Discussion Papers in Economics 196, Darmstadt University of Technology, Department of Law and Economics.
    12. Huy Quang Doan, 2019. "Trade, Institutional Quality and Income: Empirical Evidence for Sub-Saharan Africa," Economies, MDPI, vol. 7(2), pages 1-23, May.
    13. Kjetil Bjorvatn & Mohammad Reza Farzanegan, 2014. "Resource Rents, Power, and Political Stability," CESifo Working Paper Series 4727, CESifo.
    14. Simplice A Asongu, 2013. "On the Obituary of Scientific Knowledge Monopoly," Economics Bulletin, AccessEcon, vol. 33(4), pages 2718-2731.
    15. Marin, Dalia & Fabbri, Francesca, 2012. "What Explains the Rise in CEO Pay in Germany? A Panel Data Analysis for 1977-2009," CEPR Discussion Papers 8879, C.E.P.R. Discussion Papers.
    16. Samuel Fosu, 2013. "Banking Competition in Africa: Sub-regional Comparative Studies," Discussion Papers in Economics 13/12, Division of Economics, School of Business, University of Leicester, revised Jun 2013.
    17. Imam, M. & Jamasb, T. & Llorca, M. & Llorca, M., 2018. "Power Sector Reform and Corruption: Evidence from Electricity Industry in Sub-Saharan Africa," Cambridge Working Papers in Economics 1801, Faculty of Economics, University of Cambridge.
    18. Castelló-Climent, Amparo & Mukhopadhyay, Abhiroop, 2013. "Mass education or a minority well educated elite in the process of growth: The case of India," Journal of Development Economics, Elsevier, vol. 105(C), pages 303-320.
    19. Kampelmann, Stephan & Rycx, François, 2012. "The impact of educational mismatch on firm productivity: Evidence from linked panel data," Economics of Education Review, Elsevier, vol. 31(6), pages 918-931.
    20. Baharumshah, Ahmad Zubaidi & Slesman, Ly & Wohar, Mark E., 2016. "Inflation, inflation uncertainty, and economic growth in emerging and developing countries: Panel data evidence," Economic Systems, Elsevier, vol. 40(4), pages 638-657.

    More about this item

    Keywords

    Dynamic panel data models; Generalized method of moments (GMM); Monte Carlo simulation; Two-step GMM estimations.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:pra:mprapa:70627. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.