IDEAS home Printed from https://ideas.repec.org/p/iek/wpaper/1801.html
   My bibliography  Save this paper

Bias Reduction by Imputation for Linear Panel Data Models with Nonrandom Missing

Author

Listed:
  • Goeun Lee

    (Department of Economics, Korea University, Seoul, Republic of Korea)

  • Chirok Han

    (Department of Economics, Korea University, Seoul, Republic of Korea)

Abstract

When no variables are observed for endogenous non-respondents of panel data, bias correction is available only for a limited class of instrumental variable estimators, which require strong conditions for consistency and often suffer from substantial efficiency loss. In this paper we introduce a convenient alternative method of imputing the missing explanatory variables and then using standard bias-correction procedures for sample selection. Various bias-corrected estimators are derived and their performances are compared by Monte Carlo experiments. Results verify efficiency loss by the instrumental variable estimators and suggest that the imputation method is practically useful if it is applied to first-difference regression.

Suggested Citation

  • Goeun Lee & Chirok Han, 2018. "Bias Reduction by Imputation for Linear Panel Data Models with Nonrandom Missing," Discussion Paper Series 1801, Institute of Economic Research, Korea University.
    • Handle: RePEc:iek:wpaper:1801
    as

    Download full text from publisher

    File URL: http://econ.korea.ac.kr/~ri/WorkingPapers/w1801.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    4. Wooldridge, Jeffrey M., 1995. "Selection corrections for panel data models under conditional mean independence assumptions," Journal of Econometrics, Elsevier, vol. 68(1), pages 115-132, July.
    5. María Engracia Rochina-Barrachina, 1999. "A New Estimator for Panel Data Sample Selection Models," Annals of Economics and Statistics, GENES, issue 55-56, pages 153-181.
    6. repec:adr:anecst:y:1999:i:55-56:p:06 is not listed on IDEAS
    7. Chirok Han & Goeun Lee, 2017. "Efficient Estimation of Linear Panel Data Models with Sample Selection and Fixed Effects," Discussion Paper Series 1707, Institute of Economic Research, Korea University.
    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. Han, Chirok & Lee, Goeun, 2022. "Bias correction for within-group estimation of panel data models with fixed effects and sample selection," Economics Letters, Elsevier, vol. 220(C).
    2. H. Allen Klaiber & Klaus Salhofer & Stanley R. Thompson, 2017. "Capitalisation of the SPS into Agricultural Land Rental Prices under Harmonisation of Payments," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(3), pages 710-726, September.
    3. Lewbel, Arthur, 2007. "Endogenous selection or treatment model estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 777-806, December.
    4. Guastella, Gianni & Moro, Daniele & Sckokai, Paolo & Veneziani, Mario, 2014. "The capitalization of area payments into land rental prices: a panel sample selection approach," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 186375, European Association of Agricultural Economists.
    5. H. Allen Klaiber & Klaus Salhofer & Stanley R. Thompson, 2017. "Capitalisation of the SPS into Agricultural Land Rental Prices under Harmonisation of Payments," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(3), pages 710-726, September.
    6. Chirok Han & Goeun Lee, 2017. "Efficient Estimation of Linear Panel Data Models with Sample Selection and Fixed Effects," Discussion Paper Series 1707, Institute of Economic Research, Korea University.
    7. Damián Tojeiro-Rivero & Rosina Moreno & Erika Raquel Badillo, 2019. "Radical Innovations: The Role of Knowledge Acquisition from Abroad," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(2), pages 173-207, September.
    8. repec:zbw:inwedp:622016 is not listed on IDEAS
    9. Sandra Müllbacher & Wolfgang Nagl, 2017. "Labour supply in Austria: an assessment of recent developments and the effects of a tax reform," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(3), pages 465-486, August.
    10. D'Addio, Anna Cristina & De Greef, Isabelle & Rosholm, Michael, 2002. "Assessing Unemployment Traps in Belgium Using Panel Data Sample Selection Models," IZA Discussion Papers 669, Institute of Labor Economics (IZA).
    11. Jia, Lili, 2012. "Land fragmentation and off-farm labor supply in China," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 66, number 66.
    12. Gabriel Jiménez & Steven Ongena & José‐Luis Peydró & Jesús Saurina, 2014. "Hazardous Times for Monetary Policy: What Do Twenty‐Three Million Bank Loans Say About the Effects of Monetary Policy on Credit Risk‐Taking?," Econometrica, Econometric Society, vol. 82(2), pages 463-505, March.
    13. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2016. "A control function approach to estimating switching regression models with endogenous explanatory variables and endogenous switching," Journal of Econometrics, Elsevier, vol. 190(2), pages 252-266.
    14. Mora, Jhon James & Muro, Juan, 2014. "Consistent estimation in pseudo panels in the presence of selection bias," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 8, pages 1-25.
    15. Maria Alva & Alastair Gray & Borislava Mihaylova & Philip Clarke, 2014. "The Effect Of Diabetes Complications On Health‐Related Quality Of Life: The Importance Of Longitudinal Data To Address Patient Heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 487-500, April.
    16. Glenn W. Harrison & Morten I. Lau & Hong Il Yoo, 2020. "Risk Attitudes, Sample Selection, and Attrition in a Longitudinal Field Experiment," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 552-568, July.
    17. Antonietti, Roberto & Cainelli, Giulio, 2007. "Spatial Agglomeration, Technology and Outsourcing of Knowledge Intensive Business Services. Empirical Insights from Italy," Knowledge, Technology, Human Capital Working Papers 8221, Fondazione Eni Enrico Mattei (FEEM).
    18. Valizadeh, Pourya & Ng, Shu Wen, 2020. "The New school food standards and nutrition of school children: Direct and Indirect Effect Analysis," Economics & Human Biology, Elsevier, vol. 39(C).
    19. Damián Tojeiro-Rivero & Rosina Moreno & Erika Badillo, 2016. "“Breakthrough innovations: The impact of foreign acquisition of knowledge"," IREA Working Papers 201614, University of Barcelona, Research Institute of Applied Economics, revised Nov 2016.
    20. Elena Lagomarsino & Alessandro Spiganti, 2023. "Risk Aversion and the Size of Desired Debt," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 369-396, March.
    21. Rodríguez, Xosé A. & Martínez-Roget, Fidel & González-Murias, Pilar, 2018. "Length of stay: Evidence from Santiago de Compostela," Annals of Tourism Research, Elsevier, vol. 68(C), pages 9-19.

    More about this item

    Keywords

    Attrition; missing; nonresponse; bias-correction; panel data; imputation;
    All these keywords.

    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:iek:wpaper:1801. 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: Kim, Jisoo (email available below). General contact details of provider: https://edirc.repec.org/data/ierkukr.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.