Estimating a Censored Dynamic Panel Data Model With an Application to Earnings Dynamics

Luojia Hu

No 814, Working Papers from Princeton University, Department of Economics, Industrial Relations Section.

Abstract: This paper proposes a method for estimating a censored panel data model with a lagged latent dependent variable and individual-specific fixed effects. The main insight is to trim observations in such a way that a certain symmetry, which was destroyed by censoring, is restored. Based on the restored symmetry, orthogonality conditions are constructed and GMM estimation is implemented. The estimation method is used to study earnings dynamics, using matched data from the Current Population Survey and Social Security Administration (CPS- SSA) Earnings Record for a sample of men who were born in 1930-39 and living in the South dining the period of 1957-73. The SSA earnings are top-coded at the maximum social security taxable level. Although linear GMM estimation yields no difference in earnings dynamics by race, the earnings process for white men appears to be more persistent than that for black men (conditional on individual heterogeneity) after censoring is taken into account.

Keywords: panel data; censored regression; earnings dynamics (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
Date: 2000-03
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://dataspace.princeton.edu/bitstream/88435/dsp017w62f821k/1/435.pdf
Our link check indicates that this URL is bad, the error code is: 500 Internal Server Error

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pri:indrel:435

Access Statistics for this paper

More papers in Working Papers from Princeton University, Department of Economics, Industrial Relations Section. Contact information at EDIRC.
Bibliographic data for series maintained by Bobray Bordelon ().