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Nonresponse in dynamic panel data models

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  • Nicoletti, Cheti

Abstract

This paper stresses the links that exist between concepts that are used in the theory of model reduction and concepts that arise in the missing data literature. This connection motivates the extension of the missing at random (MAR) and the missing completely at random (MCAR) concepts from a static setting, as introduced by Rubin (1976), to the case of dynamic panel data models. Using this extension of the MAR and MCAR definitions, we emphasize the limits of some tests and procedures, proposed by Little (1988), Diggle (1989), Park and Davis (1993), Taris (1996) and others, to verify the ignorability of the missing data mechanism.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 132 (2006)
Issue (Month): 2 (June)
Pages: 461-489

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Handle: RePEc:eee:econom:v:132:y:2006:i:2:p:461-489

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Web page: http://www.elsevier.com/locate/jeconom

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Cited by:
  1. Stefanie Behncke, 2009. "How Does Retirement Affect Health?," University of St. Gallen Department of Economics working paper series 2009 2009-13, Department of Economics, University of St. Gallen.
  2. Bhattacharya, Debopam, 2008. "Inference in panel data models under attrition caused by unobservables," Journal of Econometrics, Elsevier, vol. 144(2), pages 430-446, June.
  3. Das, J.W.M. & Soest, A.H.O. van & Toepoel, V., 2011. "Nonparametric tests of panel conditioning and attrition bias in panel surveys," Open Access publications from Tilburg University urn:nbn:nl:ui:12-4334816, Tilburg University.
  4. Kappe, E.R. & Bijwaard, G.E., 2005. "Does work-related training reduce the discrepancy between function requirements and competencies?," Econometric Institute Research Papers EI 2005-42, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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