How to Adjust for Nonignorable Nonresponse: Calibration, Heckit or FIML?
When a survey response mechanism depends on the variable of interest measured within the same survey and observed for only part of the sample, the situation is one of nonignorable nonresponse. Ignoring the nonresponse is likely to generate significant bias in the estimates. To solve this, one option is the joint modelling of the response mechanism and the variable of interest. Another option is to calibrate each observation with weights constructed from auxiliary data. In an application where earnings equations are estimated these approaches are compared to reference estimates based on large a Swedish register based data set without nonresponse.
|Date of creation:||22 Aug 2007|
|Date of revision:|
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- Edin, Per-Anders & Fredriksson, Peter, 2000.
"LINDA - Longitudinal INdividual DAta for Sweden,"
Working Paper Series
2000:19, Uppsala University, Department of Economics.
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- Heckman, James J, 1979.
"Sample Selection Bias as a Specification Error,"
Econometric Society, vol. 47(1), pages 153-61, January.
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- Daniel S. Hamermesh & Stephen G. Donald, 2004. "The Effect of College Curriculum on Earnings: Accounting for Non-Ignorable Non-Response Bias," NBER Working Papers 10809, National Bureau of Economic Research, Inc.
- Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," Review of Economic Studies, Oxford University Press, vol. 70(1), pages 33-58.
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