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Sequential numerical integration in nonlinear state space models for microeconometric panel data

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Author Info
Florian Heiss (University of Munich, Department of Economics, Ludwigstr. 28 RG, 80539 Munich, Germany)
Abstract

This paper discusses the estimation of a class of nonlinear state space models including nonlinear panel data models with autoregressive error components. A health economics example illustrates the usefulness of such models. For the approximation of the likelihood function, nonlinear filtering algorithms developed in the time-series literature are considered. Because of the relatively simple structure of these models, a straightforward algorithm based on sequential Gaussian quadrature is suggested. It performs very well both in the empirical application and a Monte Carlo study for ordered logit and binary probit models with an AR(1) error component. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/jae.993
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File URL: http://qed.econ.queensu.ca:80/jae/2008-v23.3/
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 23 (2008)
Issue (Month): 3 ()
Pages: 373-389
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Handle: RePEc:jae:japmet:v:23:y:2008:i:3:p:373-389

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  6. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September. [Downloadable!] (restricted)
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