IDEAS home Printed from https://ideas.repec.org/p/ver/wpaper/04-2018.html
   My bibliography  Save this paper

The Joint Estimate of Singleton and Longitudinal Observations: a GMM Approach for Improved Efficiency

Author

Listed:
  • Randolph Luca Bruno

    (School of Slavonic and East European Studies, University College London)

  • Laura Magazzini

    (Department of Economics (University of Verona))

  • Marco Stampini

    (Social Protection and Health Division, Inter-American Development Bank)

Abstract

We devise an innovative methodology that allows exploiting information from singleton and longitudinal observations for the estimation of fixed effects panel data models. The approach can be applied to join cross-sectional data and longitudinal data, in order to increase estimation efficiency, while properly tackling the potential bias due to unobserved individual characteristics. Estimation is framed within the GMM context and we assess its properties by means of Monte Carlo simulations. The method is applied to an unbalanced panel of firm data to estimate a Total Factor Productivity regression based on the renown Business Environment and Enterprise Performance Survey (BEEPs) database. Under the assumption that the relationship between observed and unobserved characteristics is homogeneous across singleton and longitudinal observations (or across different samples), information from longitudinal data is used to "clean" the bias in the unpaired sample of singletons. This reduces the standard errors of the estimation (in our application, by approximately 8-9 percent) and has the potential to increase the significance of the coefficients.

Suggested Citation

  • Randolph Luca Bruno & Laura Magazzini & Marco Stampini, 2018. "The Joint Estimate of Singleton and Longitudinal Observations: a GMM Approach for Improved Efficiency," Working Papers 04/2018, University of Verona, Department of Economics.
    • Handle: RePEc:ver:wpaper:04/2018
    as

    Download full text from publisher

    File URL: http://dse.univr.it/home/workingpapers/wp2018n4.pdf
    File Function: First version
    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. Randolph Luca Bruno & MARCO STAMPINI, 2009. "Joinging Panel Data with Cross-Sections for Efficiency Gains," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 68(2), pages 149-173, July.
    3. Verbeek, Marno & Nijman, Theo, 1992. "Can Cohort Data Be Treated as Genuine Panel Data?," Empirical Economics, Springer, vol. 17(1), pages 9-23.
    4. Chad Syverson, 2011. "What Determines Productivity?," Journal of Economic Literature, American Economic Association, vol. 49(2), pages 326-365, June.
    5. Ridder, Geert, 1992. "An empirical evaluation of some models for non-random attrition in panel data," Structural Change and Economic Dynamics, Elsevier, vol. 3(2), pages 337-355, December.
    6. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    7. Kuboniwa, Masaaki & 久保庭, 眞彰 & クボニワ, マサアキ, 2011. "Russian Growth Path and TFP Changes in Light of the Estimation of Production Function using Quarterly Data," RRC Working Paper Series 30, Russian Research Center, Institute of Economic Research, Hitotsubashi University.
    8. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    9. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    10. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
    11. Hellman, Joel S. & Jones, Geraint & Kaufmann, Daniel, 2003. "Seize the state, seize the day: state capture and influence in transition economies," Journal of Comparative Economics, Elsevier, vol. 31(4), pages 751-773, December.
    12. Browning, Martin & Deaton, Angus & Irish, Margaret, 1985. "A Profitable Approach to Labor Supply and Commodity Demands over the Life-Cycle," Econometrica, Econometric Society, vol. 53(3), pages 503-543, May.
    13. Nijman, Theo & Verbeek, Marno, 1990. "Estimation of time-dependent parameters in linear models using cross-sections, panels, or both," Journal of Econometrics, Elsevier, vol. 46(3), pages 333-346, December.
    14. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Can cohort data be treated as genuine panel data?," Other publications TiSEM d4eada8f-b91c-4fe7-a58c-7, Tilburg University, School of Economics and Management.
    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. Xavier d'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers CWP40/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Paquet, Marie-France & Bolduc, Denis, 2004. "Le problème des données longitudinales incomplètes : une nouvelle approche," L'Actualité Economique, Société Canadienne de Science Economique, vol. 80(2), pages 341-361, Juin-Sept.
    3. Oguiza Tovar, Ainhoa, 2003. "Análisis descriptivo de la población ocupada en la CAPV (1993-1999)," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    4. Dolores Collado, M., 1997. "Estimating dynamic models from time series of independent cross-sections," Journal of Econometrics, Elsevier, vol. 82(1), pages 37-62.
    5. Badi Baltagi & Seuck Song, 2006. "Unbalanced panel data: A survey," Statistical Papers, Springer, vol. 47(4), pages 493-523, October.
    6. Kevin B. Moore, 2004. "The effects of the 1986 and 1993 tax reforms on self-employment," Finance and Economics Discussion Series 2004-05, Board of Governors of the Federal Reserve System (U.S.).
    7. Evren Ceritoglu, 2017. "Disentangling Age and Cohorts Effects on Home-Ownership and Housing Wealth in Turkey," Working Papers 1706, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    8. Yancy Vaillant & Esteban Lafuente & Manoj Chandra Bayon, 2019. "Early internationalization patterns and export market persistence: a pseudo-panel data analysis," Small Business Economics, Springer, vol. 53(3), pages 669-686, October.
    9. Inoue, Atsushi, 2008. "Efficient estimation and inference in linear pseudo-panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 449-466, January.
    10. Mairead Roiste & Apostolos Fasianos & Robert Kirkby & Fang Yao, 2021. "Are Housing Wealth Effects Asymmetric in Booms and Busts?," The Journal of Real Estate Finance and Economics, Springer, vol. 62(4), pages 578-628, May.
    11. Oguiza Tovar, Ainhoa, 2003. "Análisis descriptivo del desempleo en la CAPV (1993-1999)," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    12. Gardes, François & Merrigan, Philip, 2008. "Individual needs and social pressure: Evidence on the Easterlin hypothesis using repeated cross-section surveys of Canadian households," Journal of Economic Behavior & Organization, Elsevier, vol. 66(3-4), pages 582-596, June.
    13. Monika Engler, 2011. "Redistribution in Switzerland: Social Cohesion or Simple Smoothing of Lifetime Incomes?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 147(II), pages 107-155, June.
    14. Paul J. Devereux, 2007. "Small-sample bias in synthetic cohort models of labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 839-848.
    15. Gassner, Katharina, 1998. "An estimation of UK telephone access demand using Pseudo-Panel data," Utilities Policy, Elsevier, vol. 7(3), pages 143-154, November.
    16. Antonio Cutanda & José M. Labeaga & Juan A. Sanchis-Llopis, 2020. "Aggregation biases in empirical Euler consumption equations: evidence from Spanish data," Empirical Economics, Springer, vol. 58(3), pages 957-977, March.
    17. Nevo, Aviv, 2003. "Using Weights to Adjust for Sample Selection When Auxiliary Information Is Available," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 43-52, January.
    18. Rumman Khan, 2018. "Assessing cohort aggregation to minimise bias in pseudo-panels," Discussion Papers 2018-01, University of Nottingham, CREDIT.

    More about this item

    Keywords

    Panel Data; Efficient Estimation; Unobserved Heterogeneity; GMM;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:ver:wpaper:04/2018. 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: Michael Reiter (email available below). General contact details of provider: https://edirc.repec.org/data/isverit.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.