Nonlinear State-Space Models for Microeconometric Panel Data - Florian Heiss (Ludwig-Maximilians-Universität München, Volkswirtschaftliche Fakultät, 2006)
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.
urn:nbn:de:bvb:19-epub-1157-7
Keywords: C15; C23; C35; I10; 330; LDV models; panel data; state space; numerical integration; health