Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/104199
Authors: 
Heiss, Florian
Year of Publication: 
2006
Series/Report no.: 
Munich Discussion Paper 2006-24
Abstract: 
In applied microeconometric panel data analyses, time-constant random effects and first-order Markov chains are the most prevalent structures to account for intertemporal correlations in limited dependent variable models. An example from health economics shows that the addition of a simple autoregressive error terms leads to a more plausible and parsimonious model which also captures the dynamic features better. The computational problems encountered in the estimation of such models - and a broader class formulated in the framework of nonlinear state space models - hampers their widespread use. This paper discusses the application of different nonlinear filtering approaches developed in the time-series literature to these models and suggests that a straightforward algorithm based on sequential Gaussian quadrature can be expected to perform well in this setting. This conjecture is impressively confirmed by an extensive analysis of the example application.
Subjects: 
LDV models
panel data
state space
numerical integration
health
JEL: 
C15
C23
C35
I10
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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