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Title: Consistent Estimation in Pseudo Panels in the Presence of Selection Bias - Article [Dataset]      
dateReleased:
12-10-2014
downloadURL: http://dx.doi.org/10.7910/DVN/28113
ID:
doi:10.7910/DVN/28113
description:
In the presence of selection bias the traditional estimators for pseudo panel data models are inconsistent. This paper discusses a method to achieve consistency in static linear pseudo panels in the presence of selection bias and a testing procedure for sample selection bias. The authors' approach uses a bias correction term proportional to the inverse Mills ratio with argument equal to the normit of a consistent estimation of the conditional probability of being observed given cohort membership. Monte Carlo analysis shows the test does not reject the null for fixed T at a 5% significance level in finite samples. As a side effect the authors utilize the enlarged pseudo panel to provide a GMM consistent estimation of the pseudo panel parameters under rejection of the null and apply the procedure to estimate the rate of return to education in Colombia.
description:
John James Mora Rodriguez; Juan Muro, 2014, "Consistent Estimation in Pseudo Panels in the Presence of Selection Bias - Article [Dataset]", http://dx.doi.org/10.7910/DVN/28113, Harvard Dataverse, V1
name:
John James Mora Rodriguez
Juan Muro
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997