IDEAS home Printed from https://ideas.repec.org/p/bde/wpaper/1703.html
   My bibliography  Save this paper

Dynamic panel data modelling using maximum likelihood: an alternative to Arellano-Bond

Author

Listed:
  • Enrique Moral-Benito

    (Banco de España)

  • Paul Allison

    (University of pennsylvania)

  • Richard Williams

    (University of Notre Dame)

Abstract

The Arellano and Bond (1991) estimator is widely-used among applied researchers when estimating dynamic panels with fixed effects and predetermined regressors. This estimator might behave poorly in finite samples when the cross-section dimension of the data is small (i.e. small N), especially if the variables under analysis are persistent over time. This paper discusses a maximum likelihood estimator that is asymptotically equivalent to Arellano and Bond (1991) but presents better finite sample behaviour. Moreover, the estimator is easy to implement in Stata using the xtdpdml command as described in the companion paperWilliams et al. (2016), which also discusses further advantages of the proposed estimator for practitioners.

Suggested Citation

  • Enrique Moral-Benito & Paul Allison & Richard Williams, 2017. "Dynamic panel data modelling using maximum likelihood: an alternative to Arellano-Bond," Working Papers 1703, Banco de España.
    • Handle: RePEc:bde:wpaper:1703
    as

    Download full text from publisher

    File URL: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/17/Fich/dt1703e.pdf
    File Function: First version, January 2017
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nazrul Islam, 1995. "Growth Empirics: A Panel Data Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(4), pages 1127-1170.
    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. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    2. Robin Valenta & Johannes Idsø & Leiv Opstad, 2021. "Evidence of a Threshold Size for Norwegian Campsites and Its Dynamic Growth Process Implications—Does Gibrat’s Law Hold?," Economies, MDPI, vol. 9(4), pages 1-14, November.
    3. Dishop, Christopher R. & Good, Valerie, 2022. "A dynamic system of job performance with goals and leadership changes as shocks," Journal of Business Research, Elsevier, vol. 139(C), pages 602-613.
    4. Cheikh Tidiane Ndiaye & Armand Akomavo Dagoudo & Babacar Mbengue, 2021. "Growth and Income Distribution Inequalities in Sub-Saharan Africa: A Dynamic Model Approach [Croissance et inégalités de distribution des revenus en Afrique subsaharienne : une approche par les mod," Working Papers hal-03202484, HAL.
    5. André Hajek & Benedikt Kretzler & Hans-Helmut König, 2020. "Multimorbidity, Loneliness, and Social Isolation. A Systematic Review," IJERPH, MDPI, vol. 17(22), pages 1-12, November.
    6. Iles, Richard & Gatumu, Haniel & Kagunda, Samuel, 2019. "The role of poverty on economic decision-making: a model of cognitive function and heuristic use," Working Papers 2019-3, School of Economic Sciences, Washington State University.
    7. Livio Di Matteo & Robert Petrunia, 2022. "Does economic inequality breed murder? An empirical investigation of the relationship between economic inequality and homicide rates in Canadian provinces and CMAs," Empirical Economics, Springer, vol. 62(6), pages 2951-2988, June.
    8. Matthew Thomas Clement & Nathan W. Pino & Jarrett Blaustein, 2019. "Homicide Rates and the Multiple Dimensions of Urbanization: A Longitudinal, Cross-National Analysis," Sustainability, MDPI, vol. 11(20), pages 1-16, October.
    9. Kim, Hoolda & Mitra, Sophie, 2022. "Dynamics of health and labor income in Korea," The Journal of the Economics of Ageing, Elsevier, vol. 21(C).
    10. Palmer, Charles & Groom, Ben & Langton, Steve & Sileci, Lorenzo, 2022. "Biodiversity-food trade-offs when agricultural land is spared from production," LSE Research Online Documents on Economics 116614, London School of Economics and Political Science, LSE Library.
    11. Miguel Niño-Zarazúa & Ana Horigoshi & Rachel M. Gisselquist, 2022. "Aid's impact on democracy," Working Papers 248, Department of Economics, SOAS University of London, UK.
    12. José A. Gómez‐Limón & Sandra Sánchez‐Cañizares & Amalia Hidalgo‐Fernández & Ana M. Castillo‐Canalejo, 2023. "Profit and viability persistence: Evidence from the Spanish agricultural sector," Agribusiness, John Wiley & Sons, Ltd., vol. 39(4), pages 1300-1332, October.
    13. Das, Aniruddha, 2022. "Religious attendance and global cognitive function: A fixed-effects cross-lagged panel modeling study of older U.S. adults," Social Science & Medicine, Elsevier, vol. 292(C).
    14. Jing Guo & Wei Liu & Qiping Sun & Yiqun Zhou & Yonggang Wu, 2022. "Medium‐Term growth effects of Disasters‐Empirical analysis based on provincial panel data in China," Asian Economic Journal, East Asian Economic Association, vol. 36(1), pages 47-71, March.
    15. Santiago Lago-Peñas & Jorge Martinez-Vazquez & Agnese Sacchi, 2020. "Fiscal stability during the Great Recession: putting decentralization design to the test," Regional Studies, Taylor & Francis Journals, vol. 54(7), pages 919-930, July.
    16. Kim, Alex Jiyoung & Jang, Sungha & Shin, Hyun S., 2021. "How should retail advertisers manage multiple keywords in paid search advertising?," Journal of Business Research, Elsevier, vol. 130(C), pages 539-551.
    17. Fedotenkov, Igor & Idrisov, Georgy, 2021. "A supply-demand model of public sector size," Economic Systems, Elsevier, vol. 45(2).
    18. Hoolda Kim & Sophie Mitra, 2023. "Dynamics of Health and Labor Incomes in Korea," Fordham Economics Discussion Paper Series dp2023-01er:dp2023-01, Fordham University, Department of Economics.
    19. Krzysztof Beck & Ntokozo Patrick Nzimande, 2023. "Labor mobility and business cycle synchronization in Southern Africa," Economic Change and Restructuring, Springer, vol. 56(1), pages 159-179, February.
    20. Marko Crnogorac & Santiago Lago-Peñas, 2023. "An analysis of COFOG expenditures in former Yugoslavian countries," Public Sector Economics, Institute of Public Finance, vol. 47(2), pages 233-254.
    21. Alberto Vaquero-García & María Cadaval-Sampedro & Santiago Lago-Peñas, 2022. "Do Political Factors Affect Fiscal Consolidation? Evidence From Spanish Regional Governments," SAGE Open, , vol. 12(1), pages 21582440221, March.
    22. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2023. "Robust dynamic space–time panel data models using $$\varepsilon $$ ε -contamination: an application to crop yields and climate change," Empirical Economics, Springer, vol. 64(6), pages 2475-2509, June.
    23. Samina RIAZ & Venus Khim-Sen LIEW & Rossazana Bt Ab RAHIM, 2019. "The Impact of Business Cycle on Pakistani Banks Capital Buffer and Portfolio Risk," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 57-71, March.

    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. repec:zbw:rwidps:0030 is not listed on IDEAS
    2. Atolia, Manoj & Chatterjee, Santanu & Turnovsky, Stephen J., 2010. "How misleading is linearization? Evaluating the dynamics of the neoclassical growth model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1550-1571, September.
    3. 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.
    4. Ottaviano, Gianmarco & Peri, Giovanni, 2008. "Immigration and National Wages: Clarifying the Theory and the Empirics," CEPR Discussion Papers 6916, C.E.P.R. Discussion Papers.
    5. Kutuk, Yasin, 2022. "Inequality convergence: A world-systems theory approach," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 150-165.
    6. Iamsiraroj, Sasi, 2016. "The foreign direct investment–economic growth nexus," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 116-133.
    7. Kieran McQuinn & Karl Whelan, 2007. "Solow ( 1956 ) as a model of cross-country growth dynamics," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 23(1), pages 45-62, Spring.
    8. Antonio Ciccone & Giovanni Peri & Douglas Almond, "undated". "Capital, Wages, and Growth: Theory and Evidence," Working Papers 152, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Paul Raschky, 2007. "Estimating the effects of risk transfer mechanisms against floods in Europe and U.S.A.: A dynamic panel approach," Working Papers 2007-05, Faculty of Economics and Statistics, Universität Innsbruck.
    10. Vos, Rob & Frenkel, Roberto & Ocampo, José Antonio & Palma, José Gabriel & Marfán, Manuel & Ros, Jaime & Taylor, Lance & Correa, Nelson & Cimoli, Mario, 2005. "Beyond Reforms: Structural Dynamics and Macroeconomic Vulnerability," IDB Publications (Books), Inter-American Development Bank, number 347.
    11. Wen Xu, 2016. "Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters," Econometrics, MDPI, vol. 4(4), pages 1-13, October.
    12. Mulder, Peter & de Groot, Henri L.F. & Pfeiffer, Birte, 2014. "Dynamics and determinants of energy intensity in the service sector: A cross-country analysis, 1980–2005," Ecological Economics, Elsevier, vol. 100(C), pages 1-15.
    13. Aysit Tansel & Nil Demet Güngör, 2016. "Gender Effects of Education on Economic Development in Turkey," World Scientific Book Chapters, in: Nadereh Chamlou & Massoud Karshenas (ed.), Women, Work and Welfare in the Middle East and North Africa The Role of Socio-demographics, Entrepreneurship and Public Policies, chapter 3, pages 57-86, World Scientific Publishing Co. Pte. Ltd..
    14. Simplice A Asongu, 2013. "On the Obituary of Scientific Knowledge Monopoly," Economics Bulletin, AccessEcon, vol. 33(4), pages 2718-2731.
    15. Bethencourt, Carlos & Perera-Tallo, Fernando, 2020. "Human Capital, Economic Growth, and Public Expenditure," ADBI Working Papers 1066, Asian Development Bank Institute.
    16. Flora Pandya & Suresh Maind, 2017. "Panel data analysis: convergence of Indian states with infrastructure," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 19(1), pages 181-195, April.
    17. 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.
    18. Dalila Nicet-Chenaf & Eric Rougier, 2009. "Human capital and structural change: how do they interact with each others in growth," Post-Print hal-00798441, HAL.
    19. Catherine Fuss, 1999. "Mesures et tests de convergence : une revue de la littérature," Revue de l'OFCE, Programme National Persée, vol. 69(1), pages 221-249.
    20. Valérie Mignon & Christophe Hurlin, 2007. "Une synthèse des tests de cointégration sur données de panel," Économie et Prévision, Programme National Persée, vol. 180(4), pages 241-265.
    21. Gianmarco I. P. Ottaviano & Giovanni Peri, 2021. "Rethinking The Effect Of Immigration On Wages," World Scientific Book Chapters, in: Firms and Workers in a Globalized World Larger Markets, Tougher Competition, chapter 9, pages 245-290, World Scientific Publishing Co. Pte. Ltd..

    More about this item

    Keywords

    dynamic panel data; maximum likelihood estimation;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:bde:wpaper:1703. 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: Ángel Rodríguez. Electronic Dissemination of Information Unit. Research Department. Banco de España (email available below). General contact details of provider: https://edirc.repec.org/data/bdegves.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.