Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/35783
Authors: 
Keane, Michael P.
Sauer, Robert M.
Year of Publication: 
2009
Series/Report no.: 
IZA discussion papers 4054
Abstract: 
This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed initial conditions problem, as well as the more general problem of missing state variables during the sample period. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate that the estimator has good small sample properties. We apply the estimator to a model of married women's labor force participation decisions. The results show that the rarely used Polya model, which is very difficult to estimate given missing data problems, fits the data substantially better than the popular Markov model. The Polya model implies far less state dependence in employment status than the Markov model. It also implies that observed heterogeneity in education, young children and husband income are much more important determinants of participation, while race is much less important.
Subjects: 
Initial conditions
missing data
simulation
female labor force participation
JEL: 
C15
C23
C25
J13
J21
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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