Nonlinear State-Space Models for Microeconometric Panel Data

Florian Heiss

Discussion Papers in Economics from University of Munich, Department of Economics

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.

Keywords: LDV models; panel data; state space; numerical integration; health (search for similar items in EconPapers)
JEL-codes: C15 C23 C35 I10 (search for similar items in EconPapers)
Date: 2006-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Nonlinear State-Space Models for Microeconometric Panel Data (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:lmu:muenec:1157

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