Identification and EM-Estimation of Panel Data Models with Non-Ignorable Attrition and Refreshment Samples
The benefits of panel data are well-documented but missing data problems are often more severe. In particular, units that respond in the first wave may drop out of the panel after one or more periods of participation. This paper focuses on identification and Maximum Likelihood estimation of panel data models when the process that governs this so-called attrition is possibly non-ignorable. In that case, conventional estimation procedures are inconsistent. We derive a multi-period nonparametric identification result and propose estimation by an EM-algorithm that exploits the availability of refreshment samples, consisting of new units randomly drawn from the original population. This additional data source reduces the informational incompleteness of the unbalanced panel in case of non-ignorable attrition. The algorithm is stated in terms of a general population model. Issues related to specific standard panel data models are discussed seperately. Problems caused by partially observed time-varying covariates are addressed along the way.
|Date of creation:||01 Aug 2000|
|Date of revision:|
|Contact details of provider:|| Phone: 1 212 998 3820|
Fax: 1 212 995 4487
Web page: http://www.econometricsociety.org/pastmeetings.aspEmail:
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:ecm:wc2000:1569. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.